David A. Howey

Online Measurement of Battery Impedance Using Motor Controller Excitation

This paper presents a fast cost-effective technique for the measurement of battery impedance online in an application such as an electric or hybrid vehicle. Impedance measurements on lithium-ion batteries between 1 Hz and 2 kHz give information about the electrochemical reactions within a cell, which relates to the state of charge (SOC), internal temperature, and state of health (SOH). We concentrate on the development of a measurement system for impedance that, for the first time, uses an excitation current generated by a motor controller. Using simple electronics to amplify and filter the voltage and current, we demonstrate accurate impedance measurements obtained with both multisine and noise excitation signals, achieving RMS magnitude measurement uncertainties between 1.9% and 5.8%, in comparison to a high-accuracy laboratory impedance analyzer. Achieving this requires calibration of the measurement circuits, including measurement of the inductance of the current sense resistor. A statistical correlation approach is used to extract the impedance information from the measured voltage and current signals in the presence of noise, allowing a wide range of excitation signals to be used. Finally, we also discuss the implementation challenges of an SOC estimation system based on impedance.

Low-order mathematical modelling of electric double layer supercapacitors using spectral methods

Abstract This work investigates two physics-based models that simulate the non-linear partial differential algebraic equations describing an electric double layer supercapacitor. In one model the linear dependence between electrolyte concentration and conductivity is accounted for, while in the other model it is not. A spectral element method is used to discretise the model equations and it is found that the error convergence rate with respect to the number of elements is faster compared to a finite difference method. The increased accuracy of the spectral element approach means that, for a similar level of solution accuracy, the model simulation computing time is approximately 50% of that of the finite difference method. This suggests that the spectral element model could be used for control and state estimation purposes. For a typical supercapacitor charging profile, the numerical solutions from both models closely match experimental voltage and current data. However, when the electrolyte is dilute or where there is a long charging time, a noticeable difference between the numerical solutions of the two models is observed. Electrical impedance spectroscopy simulations show that the capacitance of the two models rapidly decreases when the frequency of the perturbation current exceeds an upper threshold.

Predicting the Temperature and Flow Distribution in a Direct Oil-Cooled Electrical Machine With Segmented Stator

This paper presents a computationally efficient thermo-fluid model to predict the temperature and flow distribution in an oil-cooled electrical machine with a segmented stator. The Yokeless and Segmented Armature axial flux machine was used as a case study in which a numerical model was set up and validated to within 6% of experimental results. The model was adapted to predict the temperature distribution of the segmented stator of a machine, identifying the hotspot temperatures and their location. Changes to the flow geometry on the stator temperature distribution were investigated. It was shown how by carefully controlling the flow distribution in the stator, the temperature distribution is improved and the hot spot temperature is reduced by 13 K. This benefits the machine by doubling the insulation lifetime or by increasing the current density by approximately 7%.

Comparative Experimental Investigation of Broken Bar Fault Detectability in Induction Motors

It has been shown in the past that the zero-sequence current spectrum can be reliably used to detect broken bar faults in induction motors. Previous work was carried out with extensive FEM analysis. Although it allows detailed study of spatial and time-dependent electromagnetic characteristics of induction motors, FEM is a heavily time-consuming tool and this limits full study. So, in this work, extensive experimental testing has been performed to validate the zero sequence current spectrum for detecting rotor asymmetries. Three identical induction motors have been used: one healthy, one with a broken rotor bar, and one with two broken rotor bars. The motors were tested under different voltage supply levels and with different mechanical loads. The zero-sequence current spectrum was calculated after measuring the three phase currents. It is for the first time experimentally shown that this approach offers greater diagnostic potential than traditional MCSA.

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.

Dielectric Characteristics of Electric Vehicle Traction Motor Winding Insulation Under Thermal Aging

The electric motor is the heart of the electric vehicle. It is crucial that any occurring faults are detected promptly so that a catastrophic failure is avoided. At the same time, deep knowledge of the degradation mechanisms is required to allow maximum performance at minimum cost. This paper focuses on this balance. Statistical results from measurements of unaged and accelerated aged winding insulation samples provide information about the degradation processes, enabling steps toward a reliable prognosis model of the motors remaining life.

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.

Comparative experimental investigation of broken bar fault detectability in induction motors

It has been shown in the past that the zero-sequence current spectrum can be reliably used to detect broken bar faults in induction motors. Previous work was carried out with extensive FEM analysis. Although it allows detailed study of spatial and time-dependent electromagnetic characteristics of induction motors, FEM is a heavily time-consuming tool and this limits full study. So, in this work, extensive experimental testing has been performed to validate the zero sequence current spectrum for detecting rotor asymmetries. Three identical induction motors have been used: one healthy, one with a broken rotor bar, and one with two broken rotor bars. The motors were tested under different voltage supply levels and with different mechanical loads. The zero-sequence current spectrum was calculated after measuring the three phase currents. It is for the first time experimentally shown that this approach offers greater diagnostic potential than traditional MCSA.

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.

Air-gap convection in a switched reluctance machine

Switched reluctance machines (SRMs) have recently become popular in the automotive market as they are a good alternative to the permanent magnet machines commonly employed for an electric powertrain. Lumped parameter thermal networks are usually used for thermal analysis of motors due to their low computational cost and relatively accurate results. A critical aspect to be modelled is the rotor-stator air-gap heat transfer, and this is particularly challenging in an SRM due to the salient pole geometry. This work presents firstly a review of the literature including the most relevant correlations for this geometry, and secondly, numerical CFD simulations of air-gap heat transfer for a typical configuration. A new correlation has been derived: Nu = 0.181 Tam0.207.