This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence-an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). 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 to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. 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.

This study investigates the impact of driver behaviour/driving-style on the energy consumption, state-of-charge (SOC) usage and range, of all-electric vehicles (EVs). Results from many driving cycles using a sole driver, along with those from a pre-defined ∼40 km route encompassing both urban and rural roads in Sheffield (UK) with various drivers, are given and analysed. The platform for the study is an all electric-drive Smart Fortwo ED, supplied using Zebra battery technology. Measurements of real-time quantities such as wheel speed and SOC over a number of driving trials show that energy consumption is significantly affected by driving style, and that through basic statistical analysis of acceleration profiles, for instance, a metric for assessing ‘good driving practice’ can be obtained. It is ultimately shown that the difference between driving in a moderate manner, and more aggressively, can make ∼30% difference in energy consumption – amounting to 30 g/km of CO₂ (equivalent) over the driving duty considered in this case. The results also highlight the substantial savings that can be accrued by appropriate traffic management in congested areas, by allowing the driver to minimise periods of repeated acceleration/deceleration and allow longer periods of steady-speed motion. Although a pure EV platform is used to focus the study, ultimately the results are more widely applicable to plug-in hybrid counterparts.

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.

The application of fuzzy-based control strategies has gained enormous recognition as an approach for the rapid development of effective controllers for nonlinear time-variant systems. This paper describes the preliminary research and implementation of a fuzzy logic based controller to control the wheel slip for electric vehicle antilock braking systems (ABSs). As the dynamics of the braking systems are highly nonlinear and time variant, fuzzy control offers potential as an important tool for development of robust traction control. Simulation studies are employed to derive an initial rule base that is then tested on an experimental test facility representing the dynamics of a braking system. The test facility is composed of an induction machine load operating in the generating region. It is shown that the torque-slip characteristics of an induction motor provides a convenient platform for simulating a variety of tire/road /spl mu/-/spl sigma/ driving conditions, negating the initial requirement for skid-pan trials when developing algorithms. The fuzzy membership functions were subsequently refined by analysis of the data acquired from the test facility while simulating operation at a high coefficient of friction. The robustness of the fuzzy-logic slip regulator is further tested by applying the resulting controller over a wide range of operating conditions. The results indicate that ABS/traction control may substantially improve longitudinal performance and offer significant potential for optimal control of driven wheels, especially under icy conditions where classical ABS/traction control schemes are constrained to operate very conservatively.

This paper presents an improved signal-injection-based sensorless-control method for permanent-magnet brushless AC (BLAC) motors, accounting for the influence of cross-coupling magnetic saturation between the d- and q -axes. The d- and q -axis incremental self-inductances, the incremental mutual inductance between the d-axis and q -axis, and the cross-coupling factor are determined by finite-element analysis. An experimental method is proposed for measuring the cross-coupling factor which can be used directly in the sensorless-control scheme. Both measurements and predictions show that a significant improvement in the accuracy of the rotor-position estimation can be achieved under both dynamic and steady-state operation compared with that which is obtained with the conventional signal-injection method.