Robust Estimation of Battery System Temperature Distribution Under Sparse Sensing and Uncertainty

Lin, Xinfan; Pérez, Hector E.; Siegel, Jason B.; Stefanopoulou, Anna G.

doi:10.1109/tcst.2019.2892019

Cited by 47 publications

(37 citation statements)

References 43 publications

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“…Battery cells currently deployed in automotive and aerospace applications, when considering monitoring and instrumentation, are essentially passive devices. A standard automotive pack, containing 100s of cells [1], usually includes less than 20 temperature sensors to record operating conditions [2]. The additional cost, complexity and weight associated with installing sensors post cell manufacture [3], as well as the current generation Battery Management Systems (BMS) offering limited sensor inputs, prohibit vehicle manufacturers thoroughly instrumenting packs.…”

Section: Introductionmentioning

confidence: 99%

A Smart Cell Monitoring System Based on Power Line Communication—Optimization of Instrumentation and Acquisition for Smart Battery Management

Vincent¹,

Gulsoy

Sansom

et al. 2021

IEEE Access

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Energy density of current generation battery packs is insufficient for next generation electric vehicles nor the electrification of the aerospace industry. Currently, approximately a third of energy density is lost due to ancillary demands (e.g., cooling and instrumentation) within a pack, relative to cell energy density. Smart cells, instrumented cells with sensors and circuitry, offer a means to monitor cell performance (e.g. temperature, voltage, current data). Uniquely here we demonstrate our 21700 cells instrumented with internal thermistor sensing arrays with custom miniature interface circuitry including data acquisition and communication components. This circuitry including a power line communication (PLC) system, enables sensor data to be collected and transmitted to a master controller without requiring additional wiring, and can achieve an excellent <0.005 % message error rate. The control and communication system includes the use of adaptive sampling algorithms (during identified periods of low demand, through temperature and current measurements) the cells transmit data at 0.2 Hz, increasing to 5 Hz (normal operation) or 10 Hz (beyond operating limits). This method was demonstrated via drive cycling and external heating to alert the master controller to abnormal operating conditions (rapidly, to avoid missing key features) while saving 65 % volume of data during a 90 minute experiment.

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Section: Introductionmentioning

confidence: 99%

A Smart Cell Monitoring System Based on Power Line Communication—Optimization of Instrumentation and Acquisition for Smart Battery Management

Vincent¹,

Gulsoy

Sansom

et al. 2021

IEEE Access

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“…Now consider the condition Gw ε (s) ∞ ≤ γ for the error system (10). Using Lemma 4, it becomes, for X 2 > 0…”

Section: B Main Resultsmentioning

confidence: 99%

“…This is a well-studied problem as there exist a number of works by various researchers [1]- [14]. This problem has been formulated for both continuous and discrete-time systems in different frameworks such as Kalman filter [2]- [4], H 2 /H ∞ optimal estimation [5]- [10], and in terms of CramrRao bound without any restrictions on the type of an estimator [12].…”

Section: Introductionmentioning

confidence: 99%

Sparse Sensing and Optimal Precision: An Integrated Framework for H₂/H_∞ Optimal Observer Design

Deshpande

Bhattacharya

2021

IEEE Control Syst. Lett.

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We present a framework which incorporates three aspects of the estimation problem, namely, sparse sensor configuration, optimal precision, and robustness in the presence of model uncertainty. The problem is formulated in the H∞ optimal observer design framework. We consider two types of uncertainties in the system, i.e. structured affine and unstructured uncertainties. The objective is to design an observer with a given H∞ performance index with minimal number of sensors and minimal precision values, while guaranteeing the performance for all admissible uncertainties. The problem is posed as a convex optimization problem subject to linear matrix inequalities. Numerical simulations demonstrate the application of the theoretical results presented in this work.

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“…It is rare for packs inside current generation EVs to contain more than 20 temperature sensors (usually thermocouples), often containing far fewer [11]. Without monitoring temperature at an individual cell level, any hot-spots which form inside cells cannot be identified.…”

Section: Ev Instrumentation Backgroundmentioning

confidence: 99%

Development of Smart Battery Cell Monitoring System and Characterization on a Small-Module Through In-Vehicle Power Line Communication

Vincent

Marco

2020

IEEE Access

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Robust Estimation of Battery System Temperature Distribution Under Sparse Sensing and Uncertainty

Cited by 47 publications

References 43 publications

A Smart Cell Monitoring System Based on Power Line Communication—Optimization of Instrumentation and Acquisition for Smart Battery Management

A Smart Cell Monitoring System Based on Power Line Communication—Optimization of Instrumentation and Acquisition for Smart Battery Management

Sparse Sensing and Optimal Precision: An Integrated Framework for H₂/H_∞ Optimal Observer Design

Development of Smart Battery Cell Monitoring System and Characterization on a Small-Module Through In-Vehicle Power Line Communication

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Robust Estimation of Battery System Temperature Distribution Under Sparse Sensing and Uncertainty

Cited by 47 publications

References 43 publications

A Smart Cell Monitoring System Based on Power Line Communication—Optimization of Instrumentation and Acquisition for Smart Battery Management

A Smart Cell Monitoring System Based on Power Line Communication—Optimization of Instrumentation and Acquisition for Smart Battery Management

Sparse Sensing and Optimal Precision: An Integrated Framework for H2/H∞ Optimal Observer Design

Development of Smart Battery Cell Monitoring System and Characterization on a Small-Module Through In-Vehicle Power Line Communication

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Product

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Sparse Sensing and Optimal Precision: An Integrated Framework for H₂/H_∞ Optimal Observer Design