Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs

Plett, Gregory L.

doi:10.1016/j.jpowsour.2004.02.033

Cited by 1,334 publications

(492 citation statements)

References 27 publications

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“…Voltage correction methods, typically use the OCV-SOC characteristic, either measured off-line or estimated using a real-time model of the battery to more accurately define the partial charge state of the battery relative to the nominal or actual capacity. As discussed within [50,51] Table 2 provides a detailed overview of both legislative and best-practice standards that address the performance evaluation requirements for both EV and HEV battery systems. Figure 1 presents the test duration (in hours) that is required to complete cell level energy capacity measurements from five key international standards.…”

Section: Review Of Academic Literaturementioning

confidence: 99%

Accelerated energy capacity measurement of lithium-ion cells to support future circular economy strategies for electric vehicles

Groenewald

Grandjean

Marco

2017

Renewable and Sustainable Energy Reviews

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Within the academic and industrial communities there has been an increasing desire to better understand the sustainability of producing vehicles that contain embedded electrochemical energy storage. Underpinning a number of studies that evaluate different circular economy strategies for the electric vehicle (EV) or Hybrid electric vehicle (HEV) battery system are implicit assumptions about the retained capacity or State of Health (SOH) of the battery. International standards and best-practice guides exist that address the performance evaluation of both EV and HEV battery systems. However, a common theme is that the test duration can be excessive and last for a number of hours. The aim of this research is to assess whether energy capacity measurements of Li-ion cells can be accelerated; reducing the test duration to a value that may facilitate further EOL options. Experimental results are presented that highlight it is possible to significantly reduce the duration of the battery characterisation test by 70% -90% while still retaining levels of measurement accuracy for retained energy capacity in the order of 1% for cell temperatures equal to 25 0 C. Even at elevated temperatures of 40 0 C, the peak measurement error was found to be only 3%. Based on these experimental results, a simple cost-function is formulated to highlight the flexibility of the proposed test framework. This approach would allow different organizations to prioritize the relative importance of test accuracy verses experimental test time when grading used Li-ion cells for different end-of-life (EOL) applications.

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Section: Review Of Academic Literaturementioning

confidence: 99%

Accelerated energy capacity measurement of lithium-ion cells to support future circular economy strategies for electric vehicles

Groenewald

Grandjean

Marco

2017

Renewable and Sustainable Energy Reviews

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“…In fact, it is considered among many that the use of full order models in a BMS is not feasible. 17,39,40 In order to improve the validity of circuit based models, additional components can be included to account for additional phenomena, such as diffusion resistance, 17 hysteresis, 25 temperature effects, 26,29 and selfdischarge and current inefficiencies. 29 This can be achieved by adding linear or nonlinear terms, 17 or using empirical look up tables which are functions of state of charge.…”

Section: Current Approach and The Role Of Efficient Battery Simulationmentioning

confidence: 99%

“…29 This can be achieved by adding linear or nonlinear terms, 17 or using empirical look up tables which are functions of state of charge. 25 Plett 26 found that calculating the circuit based parameters at discrete temperatures using experimental data did not extend well to temperatures that were not used to determine the parameters. In other words, the parameters did not correlate well with temperature so that linear interpolation did not provide accurate results.…”

Section: Current Approach and The Role Of Efficient Battery Simulationmentioning

confidence: 99%

“…This was partially rectified by assuming the parameters were a fourth-order function of temperature. 26 Other approaches have used reduced order models based on physical models to incorporate capacity fade effects, such as lithium plating 39 and solid electrolyte interface growth. 40 These are computationally cheap, but ignore the variation of the concentration and pore wall flux across the electrode.…”

Section: Current Approach and The Role Of Efficient Battery Simulationmentioning

confidence: 99%

“…estimating the SOC when a vehicle is turned on based on the SOC when it was last turned off, possibly accounting for self-discharge). 16,25,26,28 As state estimation for BEVs and HEVs is considerably more important and more difficult than for consumer electronics, 16 sophisticated methods have been developed, the most popular being a generalized weighted least square approach 27,28 or an extended Kalman filtering approach. 16,25,26 The generalized weighted least squares predicts the internal states of the cell by minimizing the residual between the model prediction of the measured quantities and the observed values.…”

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confidence: 99%

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Efficient Simulation and Reformulation of Lithium-Ion Battery Models for Enabling Electric Transportation

Northrop

Suthar

Ramadesigan

et al. 2014

J. Electrochem. Soc.

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Improving the efficiency and utilization of battery systems can increase the viability and cost-effectiveness of existing technologies for electric vehicles (EVs). Developing smarter battery management systems and advanced sensing technologies can circumvent problems arising due to capacity fade and safety concerns. This paper describes how efficient simulation techniques and improved algorithms can alleviate some of these problems to help electrify the transportation industry by improving the range of variables that are predictable and controllable in a battery in real-time within an electric vehicle. The use of battery models in a battery management system (BMS) is reviewed. The effect of different simulation techniques on computational cost and accuracy are also compared, and the validity of implementation in a microcontroller environment for model predictive control (MPC) is addressed. Using mathematical techniques to add more physics without losing efficiency is also discussed. Behavioral predictions can be made using mathematical models without the need to directly observe the states using expensive and time consuming physical experiments. Such predictions allow for more intelligent design of new systems, which is generally limited by the mathematical techniques used and the computational resources available. An improved modeling and simulation approach can achieve the following goals when applied to engineering systems: r More accurate predictions by using more meaningful models r Faster simulation with fewer computational resources r Optimization of design parameters r Better control, allowing aggressive performance while maintaining safetyHere we focus on the application of such principles to the use of physics-based battery models in battery management systems in electric vehicles.In recent years, battery electric vehicles (BEV) have increased in popularity to reduce the dependence on fossil fuels. Lithium-ion batteries are a popular choice as an energy storage medium for high demand applications due to their large energy density but are not utilized to their full capacity in BEV applications; operating a Li-ion battery too aggressively can lead to reduced cycle life and unpredictable thermal runaway reactions. These challenges reduce the functional capacity of the battery available for propulsion.The consumer expects the vehicle's performance and capabilities to remain uniform regardless of the state of charge or age of the battery, as they have become accustomed to internal combustion engines. When the battery is nearly depleted, it is difficult or impossible to satisfy high power demand, which is aggravated as the battery ages. To avoid these difficulties, the BMS shuts off the battery with a large amount of energy unused, so that Li-ion batteries for EVs are greatly overdesigned and carry extra weight and volume, reducing efficiency and increasing cost.1 Research is underway to better understand the internal limitations of Li-ion batteries including SEI layer growth, side * Electrochemical Society ...

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Energy Storage System Modeling and Control

Wei¹

2013

Introduction to Hybrid Vehicle System Modeling and Control

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Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs

Cited by 1,334 publications

References 27 publications

Accelerated energy capacity measurement of lithium-ion cells to support future circular economy strategies for electric vehicles

Accelerated energy capacity measurement of lithium-ion cells to support future circular economy strategies for electric vehicles

Efficient Simulation and Reformulation of Lithium-Ion Battery Models for Enabling Electric Transportation

Energy Storage System Modeling and Control

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