Author
Jon P. Christophersen
Other affiliations: Battelle Memorial Institute
Bio: Jon P. Christophersen is an academic researcher from Idaho National Laboratory. The author has contributed to research in topics: Battery (electricity) & Electrical impedance. The author has an hindex of 23, co-authored 62 publications receiving 3062 citations. Previous affiliations of Jon P. Christophersen include Battelle Memorial Institute.
Papers published on a yearly basis
Papers
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TL;DR: Models of electrochemical processes in the form of equivalent electric circuit parameters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal framework to assess the remaining useful life of complex systems.
Abstract: This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochemical processes in the form of equivalent electric circuit parameters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal framework. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.
692 citations
TL;DR: In this article, the authors examined prognostics and health management issues using battery health management of Gen 2 cells, an 18650-size lithium-ion cell, as a test case.
Abstract: In this article, we examine prognostics and health management (PHM) issues using battery health management of Gen 2 cells, an 18650-size lithium-ion cell, as a test case. We will show where advanced regression, classification, and state estimation algorithms have an important role in the solution of the problem and in the data collection scheme for battery health management that we used for this case study.
416 citations
TL;DR: Batteries were chosen as an example of a complex system whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions, where battery performance is strongly influenced by ambient environmental and load conditions and the Bayesian theory of uncertainty management provides a way to contain these problems.
Abstract: The estimation of remaining useful life (RUL) of a faulty component is at the centre of system prognostics and health management. It gives operators a potent tool in decision making by quantifying ...
397 citations
05 Nov 2007
TL;DR: The RVM, which is a Bayesian treatment of the support vector machine (SVM), is used for diagnosis as well as for model development, and the PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL.
Abstract: The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of relevance vector machine (RVM), and to state estimation via particle filters (PF), proves to be a powerful tool to integrate the diagnosis and prognosis of battery health. Accurate estimates of the state-of-charge (SOC), the state-of-health (SOH) and state-of-life (SOL) for batteries provide a significant value addition to the management of any operation involving electrical systems. This is especially true for aerospace systems, where unanticipated battery performance may lead to catastrophic failures. Batteries, composed of multiple electrochemical cells, are complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions and historical data, for which a Bayesian statistical approach is suitable. Accurate models of electro-chemical processes in the form of equivalent electric circuit parameters need to be combined with statistical models of state transitions, aging processes and measurement fidelity, need to be combined in a formal framework to make the approach viable. The RVM, which is a Bayesian treatment of the support vector machine (SVM), is used for diagnosis as well as for model development. The PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL. Validation of this approach on experimental data from Li-ion batteries is presented.
175 citations
TL;DR: In this article, the authors present the test results and life modeling of special calendar- and cycle-life tests conducted on 18650-size generation 1 (Gen 1) lithium-ion battery cells.
170 citations
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TL;DR: In this article, a review of the key technological developments and scientific challenges for a broad range of Li-ion battery electrodes is presented, and the potential/capacity plots are used to compare many families of suitable materials.
5,057 citations
TL;DR: In this paper, a Gaussian process classifier was used to estimate the probability of computerisation for 702 detailed occupations, and the expected impacts of future computerisation on US labour market outcomes, with the primary objective of analyzing the number of jobs at risk and the relationship between an occupations probability of computing, wages and educational attainment.
4,853 citations
TL;DR: The Review will consider some of the current scientific issues underpinning lithium batteries and electric double-layer capacitors.
Abstract: Energy-storage technologies, including electrical double-layer capacitors and rechargeable batteries, have attracted significant attention for applications in portable electronic devices, electric vehicles, bulk electricity storage at power stations, and “load leveling” of renewable sources, such as solar energy and wind power. Transforming lithium batteries and electric double-layer capacitors requires a step change in the science underpinning these devices, including the discovery of new materials, new electrochemistry, and an increased understanding of the processes on which the devices depend. The Review will consider some of the current scientific issues underpinning lithium batteries and electric double-layer capacitors.
2,412 citations
TL;DR: This paper systematically reviews the recent modeling developments for estimating the RUL and focuses on statistical data driven approaches which rely only on available past observed data and statistical models.
1,667 citations
TL;DR: The results suggest that the cathode material reported on could enable production of batteries that meet the demanding performance and safety requirements of plug-in hybrid electric vehicles.
Abstract: Layered lithium nickel-rich oxides, Li[Ni(1-x)M(x)]O(2) (M=metal), have attracted significant interest as the cathode material for rechargeable lithium batteries owing to their high capacity, excellent rate capability and low cost. However, their low thermal-abuse tolerance and poor cycle life, especially at elevated temperature, prohibit their use in practical batteries. Here, we report on a concentration-gradient cathode material for rechargeable lithium batteries based on a layered lithium nickel cobalt manganese oxide. In this material, each particle has a central bulk that is rich in Ni and a Mn-rich outer layer with decreasing Ni concentration and increasing Mn and Co concentrations as the surface is approached. The former provides high capacity, whereas the latter improves the thermal stability. A half cell using our concentration-gradient cathode material achieved a high capacity of 209 mA h g(-1) and retained 96% of this capacity after 50 charge-discharge cycles under an aggressive test profile (55 degrees C between 3.0 and 4.4 V). Our concentration-gradient material also showed superior performance in thermal-abuse tests compared with the bulk composition Li[Ni(0.8)Co(0.1)Mn(0.1)]O(2) used as reference. These results suggest that our cathode material could enable production of batteries that meet the demanding performance and safety requirements of plug-in hybrid electric vehicles.
1,301 citations