Forecasting the state of health of electric vehicle batteries to evaluate the viability of car sharing practices

Car sharing practices are introducing electric vehicles into their fleet. However, literature suggests that at this point shared electric vehicle systems are failing to reach satisfactory commercial viability. Potential reason for this is the effect of higher vehicle usage which is characteristic for car sharing, and the implication on the battery state of health. In this paper, we forecast state of health for two identical electric vehicles shared by two different car sharing practices. For this purpose, we use real life transaction data from charging stations and different electric vehicles’ sensors. The results indicate that insight into users’ driving and charging behaviour can provide valuable point of reference for car sharing system designers. In particular, the forecasting results show that the moment when electric vehicle battery reaches its theoretical end of life can differ in as much as ¼ of time when vehicles are shared under different conditions

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

State of health estimation of Li-ion batteries with regeneration phenomena: a similar rest time-based prognostic framework

State of health (SOH) prediction in Li-ion batteries plays an important role in intelligent battery management systems (BMS). However, the existence of capacity regeneration phenomena remains a great challenge for accurately predicting the battery SOH. This paper proposes a novel prognostic framework to predict the regeneration phenomena of the current battery using the data of a historical battery. The global degradation trend and regeneration phenomena (characterized by regeneration amplitude and regeneration cycle number) of the current battery are extracted from its raw SOH time series. Moreover, regeneration information of the historical battery derived from corresponding raw SOH data is utilized in this framework. The global degradation trend and regeneration phenomena of the current battery are predicted, and then the prediction results are integrated together to calculate the overall SOH prediction values. Particle swarm optimization (PSO) is employed to obtain an appropriate regeneration threshold for the historical battery. Gaussian process (GP) model is adopted to predict the global degradation trend, and linear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated using experimental data from the degradation tests of Li-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framewor

Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.Comment: Submitted to the Journal of Power Source

Low-cost programmable battery dischargers and application in battery model identification

This paper describes a study where a low-cost programmable battery discharger was built from basic electronic components, the popular MATLAB programming environment, and an low-cost Arduino microcontroller board. After its components and their function are explained in detail, a case study is performed to evaluate the discharger's performance. The setup is principally suitable for any type of battery cell or small packs. Here a 7.2 V NiMH battery pack including six cells is used. Consecutive discharge current pulses are applied and the terminal voltage is measured as the output. With the measured data, battery model identification is performed using a simple equivalent circuit model containing the open circuit voltage and the internal resistance. The identification results are then tested by repeating similar tests. Consistent results demonstrate accuracy of the identified battery parameters, which also confirms the quality of the measurement. Furthermore, it is demonstrated that the identification method is fast enough to be used in real-time applications

Model migration neural network for predicting battery aging trajectories

Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction

Advances in Repurposing and Recycling of Post-Vehicle-Application Lithium-Ion Batteries

Increased electrification of vehicles has increased the use of lithium-ion batteries for energy storage, and raised the issue of what to do with post-vehicle-application batteries. Three possibilities have been identified: 1) remanufacturing for intended reuse in vehicles; 2) repurposing for non-vehicle, stationary storage applications; and 3) recycling, extracting the precious metals, chemicals and other byproducts. Advances in repurposing and recycling are presented, along with a mathematical model that forecasts the manufacturing capacity needed for remanufacturing, repurposing, and recycling. Results obtained by simulating the model show that up to a 25% reduction in the need for new batteries can be achieved through remanufacturing, that the sum of repurposing and remanufacturing capacity is approximately constant across various scenarios encouraging the sharing of resources, and that the need for recycling capacity will be significant by 2030. A repurposing demonstration shows the use of post-vehicle-application batteries to support a semi-portable recycling platform. Energy is collected from solar panels, and dispensed to electrical devices as required. Recycling may be complicated: lithium-ion batteries produced by different manufacturers contain different active materials, particularly for the cathodes. In all cases, however, the collecting foils used in the anodes are copper, and in the cathodes are aluminum. A common recycling process using relatively low acid concentrations, low temperatures, and short time periods was developed and demonstrated