Robert R. Richardson

Sensorless Battery Internal Temperature Estimation Using a Kalman Filter With Impedance Measurement

This study presents a method of estimating battery- cell core and surface temperature using a thermal model coupled with electrical impedance measurement, rather than using direct surface temperature measurements. This is advantageous over previous methods of estimating temperature from impedance, which only estimate the average internal temperature. The performance of the method is demonstrated experimentally on a 2.3-Ah lithium-ion iron phosphate cell fitted with surface and core thermocouples for validation. An extended Kalman filter (EKF), consisting of a reduced-order thermal model coupled with current, voltage, and impedance measurements, is shown to accurately predict core and surface temperatures for a current excitation profile based on a vehicle drive cycle. A dual-extended Kalman filter (DEKF) based on the same thermal model and impedance measurement input is capable of estimating the convection coefficient at the cell surface when the latter is unknown. The performance of the DEKF using impedance as the measurement input is comparable to an equivalent dual Kalman filter (DKF) using a conventional surface temperature sensor as measurement input.

Gaussian process regression for forecasting battery state of health

Abstract Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.

Modular converter system for low-cost off-grid energy storage using second life li-ion batteries

Lithium ion batteries are promising for small off-grid energy storage applications in developing countries because of their high energy density and long life. However, costs are prohibitive. Instead, we consider “used” Li-ion batteries for this application, finding experimentally that many discarded laptop cells, for example, still have good capacity and cycle life. In order to make safe and optimal use of such cells, we present a modular power management system using a separate power converter for every cell. This novel approach allows individual batteries to be used to their full capacity. The power converters operate in voltage droop control mode to provide easy charge balancing and implement a battery management system to estimate the capacity of each cell, as we demonstrate experimentally.

Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage versus time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian process regression for in situ capacity estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage–time data as incremental capacity (IC) or differential voltage (DV) curves. This overcomes the need to differentiate the voltage–time data (a process that amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells, respectively. In each case, within certain voltage ranges, as little as 10 s of galvanostatic operation enables capacity estimates with approximately 2%–3% root-mean-squared error (RMSE).

Battery Capacity Estimation From Partial-Charging Data Using Gaussian Process Regression

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a novel diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which is capable of estimating the battery capacity using voltage vs. time measurements over short periods of galvanostatic operation. The approach uses Gaussian process regression to map from voltage values at a selection of uniformly distributed times, to cell capacity. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data through the lens of Incremental Capacity (IC) or Differential Voltage (DV) analysis. This overcomes both the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. Rather, GP-ICE gives insight into which portions of the voltage range are most informative about the capacity for a particular cell. We apply GP-ICE to a dataset of 8 cells, which were aged by repeated application of an ARTEMIS urban drive cycle. Within certain voltage ranges, as little as 10 seconds of charge data is sufficient to enable capacity estimates with ∼ 2% RMSE. ∗Address all correspondence to this author. INTRODUCTION Lithium ion (Li-ion) batteries experience capacity fade during use through a complex interplay of physical and chemical processes [1–3]. Knowledge of the present battery capacity is necessary to ensure reliable operation and facilitate corrective action when appropriate. To mitigate uncertainty in capacity, batteries are often over-sized and under-used, which results in increased system costs and reduced performance. Therefore, accurate online capacity estimation is a desirable function for battery management systems. There are several different approaches to capacity estimation [4, 5]. The simplest approach is direct measurement: discharging the battery from a fully charged state until the lower cut-off voltage is reached. However, this technique is difficult or impossible to apply online in most practical applications. The most common approaches in practice involve parameter estimation of battery equivalent circuit models [6–9] or electrochemical models [10–13]. These approaches have been successfully applied in many studies; however, they all require the provision of an accurate battery model. Moreover, for high fidelity models, parameter identifiability can be a major challenge [14]. Incremental capacity (IC) and differential voltage (DV) analysis have also been used for capacity estimation. These techniques have conventionally been used for detailed cell analyses, such as understanding degradation mechanisms [15, 16], although recent studies have considered the use of portions of the IC/DV curve for online capacity estimation [17–20]. In particular, Berecibar et al. [20] demonstrated cell capacity estimaProceedings of the ASME 2017 Dynamic Systems and Control Conference DSCC2017 October 11-13, 2017, Tysons, Virginia, USA