Pushing the Envelope in Battery Estimation Algorithms

Allam, Anirudh; Catenaro, Edoardo; Onori, Simona

doi:10.1016/j.isci.2020.101847

Cited by 11 publications

(12 citation statements)

References 27 publications

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“…The SPM observability matrix for the estimation of lithium concentration in both electrodes from the input and output measurements is given in (18). This section not only aims at evaluating the observability of the SPM, but, more importantly, aims to understand the practical aspects of developing an observer by evaluating the observability matrix under different electrode chemistry nonlinearities, different input conditions, and varying discretization grid points.…”

Section: Resultsmentioning

confidence: 99%

“…Plenty of research has been carried out on electrochemical model-based closed-loop observers/estimators to estimate the battery internal states, such as the solid phase lithium concentration [8]- [18], from which the battery SOC is then derived. The first step towards developing a model-based observer for any dynamic system is to verify the observability property of the model.…”

Section: A Related Literaturementioning

confidence: 99%

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Linearized Versus Nonlinear Observability Analysis for Lithium-Ion Battery Dynamics: Why Respecting the Nonlinearities Is Key for Proper Observer Design

Allam

Onori²

2021

IEEE Access

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Electrochemical model-based observers for battery state estimation play an important role in advanced battery management system design to provide accurate real-time estimates of unmueasurable critical internal variables. In this work, observability of an electrochemical battery model is studied through a thorough nonlinear observability analysis to quantify the quality of estimated variables by accounting for the rank and the condition number of the observability matrix. The paper highlights the need to carry out observability analysis on the nonlinear dynamics rather than the linearized ones. Moreover, the practical aspects of the electrochemical model' observability are investigated and quantified for different input conditions, electrode's chemistry, and discretization grid points of the partial differential equations describing the governing equations of the electrochemical dynamics.

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Section: Resultsmentioning

confidence: 99%

Section: A Related Literaturementioning

confidence: 99%

Linearized Versus Nonlinear Observability Analysis for Lithium-Ion Battery Dynamics: Why Respecting the Nonlinearities Is Key for Proper Observer Design

Allam

Onori²

2021

IEEE Access

Self Cite

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“…Battery SoH estimation and RUL prediction are beneficial to battery performance, cycle lifespan extension, maintenance, and malfunction or accident prevention ( Allam et al., 2020 ). Xiong et al.…”

Section: Operating Characteristicsmentioning

confidence: 99%

Connecting battery technologies for electric vehicles from battery materials to management

2022

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Summary Vehicle electrification has always been a hot topic and gradually become a major role in the automobile manufacturing industry over the last two decades. This paper presented comprehensive discussions and insightful evaluations of both conventional electric vehicle (EV) batteries (such as lead-acid, nickel-based, lithium-ion batteries, etc.) and the state-of-the-art battery technologies (such as all-solid-state, silicon-based, lithium-sulphur, metal-air batteries, etc.). Battery major component materials, operating characteristics, theoretical models, manufacturing processes, and end-of-life management were thoroughly reviewed. Different from other reviews focusing on theoretical studies, this review emphasized the key aspects of battery technologies, commercial applications, and lifecycle management. Useful battery managing technologies such as health prediction, charging and discharging, as well as thermal runaway prevention were thoroughly discussed. Two novel hexagon radar charts of all-round evaluations of most reigning and potential EV battery technologies were created to predict the development trend of the EV battery technologies. It showed that lithium-ion batteries (3.9 points) would be still the dominant product for the current commercial EV power battery market in a short term. However, some cutting-edge technologies such as an all-solid-state battery (3.55 points) and silicon-based battery (3.3 points) are highly likely to be the next-generation EV onboard batteries with both higher specific power and better safety performance.

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“…For this reason, the battery management system (BMS) is tasked with the responsibility of monitoring the critical battery internal variables such as SOC, state-of-health, state-of-energy, etc. [3]. In addition to software, the BMS includes hardware composed of many microcontroller units.…”

Section: Introductionmentioning

confidence: 99%

Real-Time State-of-Charge Estimation Using an Embedded Board for Li-Ion Batteries

et al. 2022

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With the use of batteries increases, the complexity of battery management systems (BMSs) also rises. Thus, assessing the functionality of BMSs and performance of the BMS hardware is of utmost importance. Testing with embedded boards at an early stage of BMS development is a pragmatic approach for developing a BMS because it is cost- and time-efficient and considers hardware performance. In this study, we tested and analyzed the real-time state-of-charge (SOC) estimation using a test platform with limited CPU performance as well as memory resources of the embedded board. We collected battery data on a single-cell basis using a first-order RC equivalent circuit and achieved an accuracy of 95% compared to the measured data obtained using actual battery tests. The SOC estimation method applies the extended Kalman filter (EKF) and unscented Kalman filter (UKF). The experiment was performed on the real-time test platform, with 1%, 2%, and 5% noise in the measurement data. The algorithm complexity and hardware implementation were evaluated in terms of the resources used and processing speed. Although the EKF is cost-effective, its error rate increases by 5% with noise interference. The UKF exhibits high accuracy and noise robustness; however, it has a high resource occupancy.

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Pushing the Envelope in Battery Estimation Algorithms

Cited by 11 publications

References 27 publications

Linearized Versus Nonlinear Observability Analysis for Lithium-Ion Battery Dynamics: Why Respecting the Nonlinearities Is Key for Proper Observer Design

Linearized Versus Nonlinear Observability Analysis for Lithium-Ion Battery Dynamics: Why Respecting the Nonlinearities Is Key for Proper Observer Design

Connecting battery technologies for electric vehicles from battery materials to management

Real-Time State-of-Charge Estimation Using an Embedded Board for Li-Ion Batteries

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