A multi-mode electric vehicle range estimator based on driving pattern recognition

Limited driving range and availability of charging infrastructures are still among the main barriers of adoption of electric vehicles (EVs) in the market. Combination of those limiting factors causes ‘range anxiety’ in EV users. While different EV battery technologies and charging infrastructures are under development, one short-term solution to reduce EV users’ range anxiety is to provide the EV user with an accurate range estimation. In this study, an EV range estimation technique is proposed that recognises the current driving pattern and then classifies it into one of the predefined clusters (driving modes). The future energy consumption per kilometre is then tuned according to the average energy consumption of each cluster. Having an updated energy consumption rate, the EV range is calculated based on the battery state-of-charge. Different features are considered for driving pattern clustering where ‘average speed’ and ‘average power’ were identified as the best choices for this application. The effectiveness of the proposed EV range estimator is validated using real driving data that gives an average error of 9% in EV energy consumption estimation ahea

Development of a hybrid adaptive neuro-fuzzy inference system with coulomb-counting state-of-charge estimator for lithium–sulphur battery

This study presents the development of an improved state of charge (SOC) estimation technique for lithium–sulphur (Li–S) batteries. This is a promising technology with advantages in comparison with the existing lithium-ion (Li-ion) batteries such as lower production cost and higher energy density. In this study, a state-of-the-art Li–S prototype cell is subjected to experimental tests, which are carried out to replicate real-life duty cycles. A system identification technique is then used on the experimental test results to parameterize an equivalent circuit model for the Li–S cell. The identification results demonstrate unique features of the cell’s voltage-SOC and ohmic resistance-SOC curves, in which a large flat region is observed in the middle SOC range. Due to this, voltage and resistance parameters are not sufficient to accurately estimate SOC under various initial conditions. To solve this problem, a forgetting factor recursive least squares (FFRLS) identification technique is used, yielding four parameters which are then used to train an adaptive neuro-fuzzy inference system (ANFIS). The Sugeno-type fuzzy system features four inputs and one output (SOC), totalling 375 rules. Each of the inputs features Gaussian-type membership functions while the output is of a linear type. This network is then combined with the coulomb-counting method to obtain a hybrid estimator that can accurately estimate SOC for a Li–S cell under various conditions with a maximum error of 1.64%, which outperforms the existing methods of Li–S battery SOC estimation

Lithium-sulfur cell state of charge estimation using a classification technique

Lithium-Sulfur (Li-S) batteries are a promising next-generation technology providing high gravimetric energy density compared to existing lithium-ion (Li-ion) technologies in the market. The literature shows that in Li-S, estimation of state of charge (SoC) is a demanding task, in particular due to a large flat section in the voltage-SoC curve. This study proposes a new SoC estimator using an online parameter identification method in conjunction with a classification technique. This study investigates a new prototype Li-S cell. Experimental characterization tests are conducted under various conditions; the duty cycle – intended to represent a real-world application – is based on an electric city bus. The characterization results are then used to parameterize an equivalent-circuit-network (ECN) model, which is then used to relate real-time parameter estimates derived using a Recursive Least Squares (RLS) algorithm to state of charge using a Support Vector Machine (SVM) classifier to estimate an approximate SoC range. The estimate is used together with a conventional coulomb-counting technique to achieve continuous SoC estimation in real-time. It is shown that this method can provide an acceptable level of accuracy with less than 3% error under realistic driving conditions

An experimental study on prototype lithium-sulfur cells for ageing analysis and state-of-health estimation

Lithium-Sulfur (Li-S) batteries offer a potential for higher gravimetric energy density in comparison to lithium-ion batteries. Since they behave quite different from lithium-ion batteries, distinctive approaches to state estimation and battery management are required to be developed specifically for them. This paper describes an experimental work to model and perform real-time estimation of the progression of use-induced ageing in prototype Li-S cells. To do that, state-of-the-art 19 Ah Li-S pouch cells were subject to cycling tests in order to determine progressive changes in parameters of a nonlinear equivalent-circuit-network (ECN) model due to ageing. A state-of-health (SoH) estimation algorithm was then designed to work based on identifying ECN parameters using Forgetting-Factor Recursive Least Squares (FFRLS). Two techniques, nonlinear curve fitting and Support Vector Machine (SVM) classification, were used to generate SoH values according to the identified parameters. The results demonstrate that Li-S cell’s SoH can be estimated with an acceptable level of accuracy of 96.7% using the proposed method under realistic driving conditions. Another important outcome was that the ‘power fade’ in Li-S cells happens at a much slower rate than the ‘capacity fade’ which is a useful feature for applications where consistency of power delivery is important

Charging characterization of a high‐capacity lithium‐sulfur pouch cell for state estimation–an experimental approach

Lithium-Sulfur (Li-S) battery is a next-generation technology, which is promising for applications that require higher energy density in comparison to the available lithium-ion batteries. Along with the ongoing research on Li-S cell material development and manufacturing to improve this technology, engineers are also working on Li-S battery management systems (BMS). The existing BMS algorithms, which are developed for lithium-ion batteries, are not useable for the Li-S mainly due to its constant voltage plateau during the discharge phase. As a result, the Li-S system has poor observability during discharge, which limits the BMS functionality that can be implemented from discharge information alone, and it is worth considering if information from charging is useful. In this study, the charging behavior of a high-capacity pouch cell is investigated and characterized for the purpose of state estimation in a BMS. Several tests are conducted on prototype Li-S cells at different temperatures and age levels. An online feature extraction method is then used in combination with a classification technique to estimate the cell's states during charging. The proposed charging estimators can provide accurate initialization for state estimation accuracy during discharge by providing good estimates of the post-charging state of charge (ie, around 3%) and capacity after fading (ie, around 2%)

A MATLAB graphical user interface for battery design and simulation; from cell test data to real-world automotive simulation

This paper describes a graphical user interface (GUI) tool designed to support cell design and development of manufacturing processes for an automotive battery application. The GUI is built using the MATLAB environment and is able to load and analyze raw test data as its input. After data processing, a cell model is fitted to the experimental data using system identification techniques. The cell model's parameters (such as open-circuit-voltage and ohmic resistance) are displayed to the user as functions of state of charge, providing a visual understanding of the cell's characteristics. The GUI is also able to simulate the performance of a full battery pack consisting of a specified number of single cells using standard driving cycles and a generic electric vehicle model. After a simulation, the battery designer is able to see how well the vehicle would be able to follow the driving cycle using the tested cells. Although the GUI is developed for an automotive application, it could be extended to other applications as well. The GUI has been designed to be easily used by non-simulation experts (i.e. battery designers or electrochemists) and it is fully automated, only requiring the user to supply the location of raw test data

Electric vehicle energy consumption estimation for a fleet management system

Accurate estimation of vehicles’ energy consumption is a demanding task. It might not be so critical for conventional vehicles because of their high travel range however, this is something important for electric vehicles (EVs). On the other hand, EVs with less energy on board, need more accurate energy management systems. This study focuses on the development of an energy consumption estimation model to be used in an EV fleet management system (FMS). The proposed estimator consists of a vehicle model, a driver model, and terrain models. It is demonstrated that a combination of these three parts can provide an accurate estimation of EV energy consumption on a particular route. As part of this study, a commercially-available passenger car is modelled using MATLAB/Simulink. A number of specific routes are selected for EV road testing to be driven for simulation model verification. In the second part of this study, the impact of energy consumption estimation accuracy is investigated at a larger scale for a fleet of EVs. It is quantitatively demonstrated how much sensitive is the performance of a FMS to the accuracy of the energy estimator. Simulation results have shown that the total energy consumption of an EV fleet is decreased significantly by improving the estimation accuracy. It is also demonstrated how the uncertainties in EV energy consumption estimation limits the overall performance of a FMS

Deterministic observability calculations for zero-dimensional models of lithium–sulfur batteries

Among the various energy storage technologies under development, the lithium‑sulfur (Li–S) battery has considerable promise due to its higher theoretical energy density, small environmental footprint, and low projected costs. One of the main challenges posed by Li–S is the need for a battery management system (BMS) that can accommodate the system's complex multi-step redox behaviours; conventional approaches for lithium-ion batteries do not transfer. Most existing approaches rely on equivalent circuit network models, but there is growing interest in ‘zero-dimensional’ electrochemical models which can potentially give insights into the relative polysulfide species concentrations present at any given time. To be useful for state estimation, a model must be ‘observable’: it must be possible to uniquely determine the internal state through observation of the system's behaviour over time. Previous studies have assessed observability using numerical methods, which is an approximation. This study derives an analytic expression for the observability criterion, which allows greater confidence in the results. The analytic observability criterion is then validated against a numerical comparator. A zero-dimensional model from the literature is translated into an ordinary differential equation (ODE) form to define the state variables matrix A, the output matrix C, and subsequently the observability matrix O. These are compared to simulated numerical equivalents. In addition, the sensitivity of the numerical process has been demonstrated. The results have the potential to offer greater confidence in conclusions around observability, which in turn gives greater confidence in the effects of any algorithms based on them.This work was funded by the European Commission under grant agreement 814471, and the Innovate UK under grant TS/R013780/1

Recent progress and emerging application areas for lithium-sulfur battery technology

Electrification is progressing significantly within the present and future vehicle sectors such as large commercial vehicles (e. g. trucks and busses), high altitude long endurance (HALE), high altitude pseudo satellites (HAPS), and electric vertical take‐off and landing (eVTOL). The battery systems performance requirements differ across these applications in terms of power, cycle life, system cost, etc. However, the need for high gravimetric energy density, 400 Wh kg−1 and beyond, is common across them all, since it will enable vehicles to achieve extended range, longer mission duration, lighter weight or increased payload. The system level requirements of these emerging applications can be broken down into the component level developments required to integrate Li‐S technology as the power system of choice. In order to adapt the batteries’ properties, such as energy and power density, to the respective application, the academic research community has a key role to play in component level development. However, materials and component research must be conducted within the context of a viable Li‐S cell system. Herein, the key performance benefits, limitations, modelling and recent progress of the Li‐S battery technology and its adaption towards real world application are discusse

Investigation of the effect of temperature on lithium-sulfur cell cycle life performance using system identification and x-ray tomography

In this study, cycle life performance of a prototype lithium-sulfur (Li−S) pouch cell is investigated using system identification and X-ray tomography methods. Li−S cells are subjected to characterization and ageing tests while kept inside a controlled-temperature chamber. After completing the experimental tests, two analytical approaches are used: i) The parameter variations of an equivalent-circuit model due to ageing are determined using a system identification technique. ii) Physical changes of the aged Li−S cells are analyzed using X-ray tomography. The results demonstrate that Li−S cell's degradation is significantly affected by temperature. Comparing to 10 °C, Li−S cell capacity fade happens 1.4 times faster at 20 °C whereas this number increases to 3.3 at 30 °C. In addition, X-ray results show a significant swelling when temperature rises from 10 to 20 °C, correspondingly the gas volume increases from 13 to 62 mm3.Innovate UK: TS/R013780/1. European Union funding: 814471. Engineering and Physical Sciences Research Council (EPSRC): EP/S003053/1, FIRG014, FIRG027