Observer techniques for estimating the state-of-charge and state-of-health of VRLABs for hybrid electric vehicles

The paper describes the application of observer-based state-estimation techniques for the real-time prediction of state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, an approach based on the well-known Kalman filter, is employed, to estimate SoC, and the subsequent use of the EKF to accommodate model non-linearities to predict battery SoH. The underlying dynamic behaviour of each cell is based on a generic Randles' equivalent circuit comprising of two-capacitors (bulk and surface) and three resistors, (terminal, transfer and self-discharging). The presented techniques are shown to correct for offset, drift and long-term state divergence-an unfortunate feature of employing stand-alone models and more traditional coulomb-counting techniques. Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC, with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior with SoC being estimated to be within 1% of measured. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack

New battery model and state-of-health determination through subspace parameter estimation and state-observer techniques

This paper describes a novel adaptive battery model based on a remapped variant of the well-known Randles' lead-acid model. Remapping of the model is shown to allow improved modeling capabilities and accurate estimates of dynamic circuit parameters when used with subspace parameter-estimation techniques. The performance of the proposed methodology is demonstrated by application to batteries for an all-electric personal rapid transit vehicle from the Urban Light TRAnsport (ULTRA) program, which is designated for use at Heathrow Airport, U. K. The advantages of the proposed model over the Randles' circuit are demonstrated by comparisons with alternative observer/estimator techniques, such as the basic Utkin observer and the Kalman estimator. These techniques correctly identify and converge on voltages associated with the battery state-of-charge (SoC), despite erroneous initial conditions, thereby overcoming problems attributed to SoC drift (incurred by Coulomb-counting methods due to overcharging or ambient temperature fluctuations). Observation of these voltages, as well as online monitoring of the degradation of the estimated dynamic model parameters, allows battery aging (state-of-health) to also be assessed and, thereby, cell failure to be predicted. Due to the adaptive nature of the proposed algorithms, the techniques are suitable for applications over a wide range of operating environments, including large ambient temperature variations. Moreover, alternative battery topologies may also be accommodated by the automatic adjustment of the underlying state-space models used in both the parameter-estimation and observer/estimator stages

Novel battery model of an all-electric personal rapid transit vehicle to determine state-of-health through subspace parameter estimation and a Kalman Estimator

Abstract--The paper describes a real-time adaptive battery model for use in an all-electric Personal Rapid Transit vehicle. Whilst traditionally, circuit-based models for lead-acid batteries centre on the well-known Randles’ model, here the Randles’ model is mapped to an equivalent circuit, demonstrating improved modelling capabilities and more accurate estimates of circuit parameters when used in Subspace parameter estimation techniques. Combined with Kalman Estimator algorithms, these techniques are demonstrated to correctly identify and converge on voltages associated with the battery State-of-Charge, overcoming problems such as SoC drift (incurred by coulomb-counting methods due to over-charging or ambient temperature fluctuations). Online monitoring of the degradation of these estimated parameters allows battery ageing (State-of-Health) to be assessed and, in safety-critical systems, cell failure may be predicted in time to avoid inconvenience to passenger networks. Due to the adaptive nature of the proposed methodology, this system can be implemented over a wide range of operating environments, applications and battery topologies

Modelling and state-of-charge estimation for ultracapacitors in electric vehicles

University of Technology Sydney. Faculty of Engineering and Information Technology.In order to cope with the global challenges like fossil fuel depletion and environmental pollution, electrified vehicles (EVs) have been widely accepted as an enabling option for future ground mobility. In comparison to conventional combustion engine vehicles, EVs have the advantage of high efficiency, environment-friendly operation and excellent control flexibility. There is a proviso here that the electricity used by the EV is from a green source such as hydro, wind or solar. The energy storage system (ESS) is a key ingredient of an EV, and significantly affects its driving performance and cost-effectiveness. The exploration of a vehicular ESS poses a formidable challenge, because of high power/energy demands and unpredictable driving environments. Li-ion batteries represent a main choice for this use, but suffer the drawbacks of low power density and poor recyclability. Recently, ultracapacitors (UCs), also referred to as supercapacitors (SCs) or electric double-layer capacitors (EDLCs), have gained increasing attention in the energy storage community, thanks to their high power density, high efficiency, fast charge, wide temperature window and excellent recyclability. These advantages make UCs a good augmentation to high-energy ESSs (e.g., fuel cells, lithium-ion batteries). This combination represents a hybrid energy storage system (HESS) that can fully leverage the synergistic benefits of each constituent device. To ensure efficient, reliable and safe operations of UC systems, numerous challenges including modelling and characterization, and State-of-Charge (SOC) estimation should be effectually surmounted. In order to meet the above mentioned challenges, the main research presented in this dissertation includes: 1. A special test rig for UC characteristic investigation has been established. A test procedure is proposed to collect comprehensive test data. A plethora of tests have been conducted on this test rig including capacity calibration, experimental impedance investigation under different temperatures and SOC values, and dynamic cycling including pulse tests and driving-cycle-based tests under different temperatures, resulting in a wide-ranging UC database. 2. The impedance characteristics of UCs are experimentally investigated under different temperatures and SOC values. The results show that the impedance is highly sensitive to temperature and SOC; and the temperature effect is more significant. In particular, the coupling effect between the temperature and SOC is illustrated, and the high-efficiency SOC window is highlighted. 3. For UC modelling, three commonly used equivalent circuit models are systematically examined in terms of model accuracy, complexity and robustness in the context of EV applications. A genetic algorithm (GA) is employed to extract the optimal model parameters based on the Hybrid Pulse Power Characterization (HPPC) test data. The performance of these models is then evaluated and compared by measuring the model complexity, accuracy, and robustness against “unseen” data collected in the Dynamic Stress Test (DST) and a self-designed pulse (SDP) test. The validation results show that the dynamic model has the best overall performance for EV applications. 4. Online parameter identification of UC models is researched. The extended Kalman Filter (EKF) is proposed to recursively estimate the model parameters using the DST dataset, in which the dynamic model is used to represent the UC dynamics. The effectiveness and robustness of the proposed method is validated using another driving-cycle-based dataset. 5. A novel robust H-infinity observer is presented to realize UC SOC estimation in real-time. In comparison to the state-of-the-art Kalman filter-based (KF-based) methods, the developed robust scheme can ensure high estimation accuracy without prior knowledge of process and measurement noise statistical properties. More significantly, the proposed H-infinity observer proves to be more robust to modelling uncertainties arising from the change of thermal conditions and/or cell health status. 6. A novel fractional-order model is put forward to emulate the UC dynamics. In contrast to integer-order models, the presented fractional-order model has the merits of better model accuracy and fewer parameters. It consists of a series resistor, a constant-phase-element (CPE), and a Warburg-like element. The model parameters are optimally extracted using GA, based on the time-domain Federal Urban Driving Schedule (FUDS) test data acquired through the established test rig. By means of this fractional-order model, a fractional Kalman filter is synthesized to recursively estimate the UC SOC. Validation results show that the proposed fractional-order modelling and state estimation scheme is accurate and outperforms the current practice based on integral-order techniques. 7. An optimal HESS sizing method using a multi-objective optimization algorithm is presented, in which the primary goal is reducing the ESS cost and weight while prolonging battery life. To this end, a battery state-of-health (SOH) model is incorporated to quantitatively investigate the impact of component sizing on battery life. The wavelet-transform-based power management algorithm is adopted to realize the power coordination between the battery and UC packs. The results provide prudent insights into HESS sizing with different emphases

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC

Kalman-variant estimators for state of charge in lithium-sulfur batteries

Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of ‘standard’ lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and ‘Coulomb counting’ are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit–network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort

Electric vehicle battery model identification and state of charge estimation in real world driving cycles

This paper describes a study demonstrating a new method of state-of-charge (SoC) estimation for batteries in real-world electric vehicle applications. This method combines realtime model identification with an adaptive neuro-fuzzy inference system (ANFIS). In the study, investigations were carried down on a small-scale battery pack. An equivalent circuit network model of the pack was developed and validated using pulse-discharge experiments. The pack was then subjected to demands representing realistic WLTP and UDDS driving cycles obtained from a model of a representative electric vehicle, scaled match the size of the battery pack. A fast system identification technique was then used to estimate battery parameter values. One of these, open circuit voltage, was selected as suitable for SoC estimation, and this was used as the input to an ANFIS system which estimated the SoC. The results were verified by comparison to a theoretical Coulomb-counting method, and the new method was judged to be effective. The case study used a small 7.2 V NiMH battery pack, but the method described is applicable to packs of any size or chemistry

Estimating the state of charge of lithium-ion batteries using different noise inputs

State of charge estimation (SOC) is the most significant functionality of a vehicle's battery management system (BMS). The methods for this estimation are conventionally oriented towards model-based methods. As part of this paper, we introduce a first order equivalent circuit estimation approach known as the Thevenin model, along with an extended Kalman filter (EKF) approach to accurately estimate the SOC. We then deploy and simulate it in MATLAB by using a reference load profile from the new European driving cycle (NEDC). Afterwards, the simulation results are reviewed based on various initial noise values, and the results are compared to those of other EKF algorithms. According to the results, SOC estimation accuracy has significantly increased as a result of the improvements made. Specifically, the root-mean-square error decreased from 0.0068 to 0.0020