Accurate prediction of range of an electric vehicle (EV) is a significant issue and a key market qualifier. EV range forecasting can be made practicable through the application of advanced modelling ...and estimation techniques. Battery modelling and state-of-charge estimation methods play a vital role in this area. In addition, battery modelling is essential for safe charging/discharging and optimal usage of batteries. Much existing work has been carried out on incumbent Lithium-ion (Li-ion) technologies, but these are reaching their theoretical limits and modern research is also exploring promising next-generation technologies such as Lithium–Sulphur (Li–S). This study reviews and discusses various battery modelling approaches including mathematical models, electrochemical models and electrical equivalent circuit models. After a general survey, the study explores the specific application of battery models in EV battery management systems, where models may have low fidelity to be fast enough to run in real-time applications. Two main categories are considered: reduced-order electrochemical models and equivalent circuit models. The particular challenges associated with Li–S batteries are explored, and it is concluded that the state-of-the-art in battery modelling is not sufficient for this chemistry, and new modelling approaches are needed.
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.
Overtaking in motorsport races is a crucial action in order to gain a higher position during a race. In Formula-E, it not only requires precisely performed overtaking manoeuvres but also requires ...appropriate energy management which is also a crucial element in race strategy due to the limited total amount of energy on board. This study proposes the use of optimal control techniques for feasibility and energy management analysis for such a complex problem. The analysis involves two cars on a track and the goal is to find the optimal overtaking strategy under various scenarios. The results demonstrate that overtaking can be infeasible despite that there is a potential speed advantage over the target car. An overtaking manoeuvre can be executed efficiently by using higher propulsion power. When an overtaking is feasible, the energy-optimal overtaking position varies according to different initial conditions.
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.
•Li-S batteries differ to Li-ion batteries, and require specific state of charge estimation.•We discuss the limitations of standard SoC estimation methods with Li-S.•A set of applicable state-of-charge estimators for Li-S batteries is developed.•The extended Kalman Filter (KF), unscented KF and Particle filter are applied.•The performance of the applied recursive Bayesian filters is evaluated.
This paper presents a framework for battery modeling in online, real-time applications where accuracy is important but speed is the key. The framework allows users to select model structures with the ...smallest number of parameters that is consistent with the accuracy requirements of the target application. The tradeoff between accuracy and speed in a battery model identification process is explored using different model structures and parameter-fitting algorithms. Pareto optimal sets are obtained, allowing a designer to select an appropriate compromise between accuracy and speed. In order to get a clearer understanding of the battery model identification problem, "identification surfaces" are presented. As an outcome of the battery identification surfaces, a new analytical solution is derived for battery model identification using a closed-form formula to obtain a battery's ohmic resistance and open circuit voltage from measurement data. This analytical solution is used as a benchmark for comparison of other fitting algorithms and it is also used in its own right in a practical scenario for state-of-charge estimation. A simulation study is performed to demonstrate the effectiveness of the proposed framework and the simulation results are verified by conducting experimental tests on a small NiMH battery pack.
In order to design an advanced human-automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper, a dynamic model of ...drivers' neuromuscular interaction with a steering wheel is first established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver-steering-wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss-Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions, and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles.
Energy and thermal management is a crucial element in Formula-E race strategy development. In this study, the race-level strategy development is formulated into a Markov decision process (MDP) ...problem featuring a hybrid-type action space. Deep Deterministic Policy Gradient (DDPG) reinforcement learning is implemented under distributed architecture Ape-X and integrated with the prioritized experience replay and reward shaping techniques to optimize a hybrid-type set of actions of both continuous and discrete components. Soft boundary violation penalties in reward shaping, significantly improves the performance of DDPG and makes it capable of generating faster race finishing solutions. The new proposed method has shown superior performance in comparison to the Monte Carlo Tree Search (MCTS) with policy gradient reinforcement learning, which solves this problem in a fully discrete action space as presented in the literature. The advantages are faster race finishing time and better handling of ambient temperature rise.
•Targeting the most popular race strategy problem in Formula-E races.•Propose a novel race strategy approach using distributed reinforcement learning architecture.•Reward shaping improves the reinforcement learning performance.•Modification of actor neural network layout for hybrid-type action generation.
Compared to lithium-ion batteries, lithium-sulfur (Li-S) batteries potentially offer greater specific energy density, a wider temperature range of operation, and safety benefits, making them a ...promising technology for energy storage systems especially in automotive and aerospace applications. Unlike lithium-ion batteries, there is not a mature discipline of equivalent circuit network (ECN) modelling for Li-S. In this study, ECN modelling is addressed using formal `system identification' techniques. A Li-S cell's performance is studied in the presence of different charge/discharge rates and temperature levels using precise experimental test equipment. Various ECN model structures are explored, considering the tradeoffs between accuracy and speed. It was concluded that a `2RC' model is generally a good compromise, giving good accuracy and speed. Model parameterization is repeated at various state-of-charge (SOC) and temperature levels, and the effects of these variables on Li-S cell's ohmic resistance and total capacity are demonstrated. The results demonstrate that Li-S cell's ohmic resistance has a highly nonlinear relationship with SOC with a break-point around 75% SOC that distinguishes it from other types of battery. Finally, an ECN model is proposed which uses SOC and temperature as inputs. A sensitivity analysis is performed to investigate the effect of SOC estimation error on the model's accuracy. In this analysis, the battery model's accuracy is evaluated at various SOC and temperature levels. The results demonstrate that the Li-S cell model has the most sensitivity to SOC estimation error around the break-point (around 75% SOC) whereas in the middle SOC range, from 20% to 70%, it has the least sensitivity.
Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as ...battery management. Computationally simple models are vital for exploitation. This paper proposes a non-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a ‘behavioural’ interpretation of the ECN model; as Li-S exhibits a ‘steep’ open-circuit voltage (OCV) profile at high states-of-charge, identification methods are designed to take into account OCV changes during current pulses. The prediction-error minimization technique is used. The model is parameterized from laboratory experiments using a mixed-size current pulse profile at four temperatures from 10 °C to 50 °C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict the behaviour of a validation data set representing an automotive NEDC driving cycle, the terminal voltage predictions are judged accurate with a root mean square error of 32 mV.
Electrification is progressing significantly within the present and future vehicle sectors such as large commercial vehicles (e.g., trucks and buses), high‐altitude long endurance (HALE), ...high‐altitude pseudosatellites (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, as it enables vehicles to achieve extended range, a longer mission duration, lighter weight, or increased payload. The system‐level requirements of these emerging applications are broken down into the component‐level developments required to integrate Li–S technology as the power system of choice. To adapt 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, modeling, and recent progress of the Li–S battery technology and its adaption toward real‐world application are discussed.
The advantages, limitations, and progress of Li–S batteries are described. Approaches to extend cycle life, energy, power, and safety are further discussed. Recent advances regarding modelling and battery management of these batteries are summarized, and the requirements for as well as the integration of Li–S prototype cells in present and future real world applications is elaborated.
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