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Rechargeable batteries are critical components in many electrical systems nowadays. One has to ensure reliable diagnosis and assessment of the installed batteries for smooth and safe operations. Assessment of the remaining capacity of a battery is crucial diagnostic information. A battery management system (BMS) needs to reliably report the ability...
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Citations
... Contingency management for battery systems starts with prognostics and health management (PHM), and several researchers have studied precise estimation of battery remaining useful life (RUL) computed from battery SoC. Such methods have used extended Kalman filter (EKF) [19], unscented Kalman filter (UKF) [20], unscented transform [21], particle filter [22], neural network [23], and Gaussian process regression (GPR) [24,25] formulations. Sharma and Atkins [26] proposed a multibattery reconfiguration for UASs using a prognostics-informed MDP. ...
Uncrewed aircraft systems (UASs) are being increasingly adopted for a variety of applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper proposes risk-aware contingency management autonomy to prevent an accident in the event of component malfunction, specifically propulsion unit failure and/or battery degradation. The proposed autonomy is modeled as a Markov decision process (MDP), whose solution is a contingency management policy that appropriately executes emergency landing, flight termination, or continuation of planned flight actions. Motivated by the potential for errors in fault/failure indicators, the partial observability of the MDP state space is investigated. The performance of optimal policies is analyzed over varying observability conditions in a high-fidelity simulator. Results indicate that both partially observable MDP and maximum a posteriori MDP policies had similar performance over different state observability criteria, given the nearly deterministic state transition model.
... The safe region contains all the desired aspects; however, the failure region represents the undesired manners. Therefore, several battery harmful outcomes are avoided by detecting the transition from safe region to failure region [4], [5], [6], [7]. The battery capacity represented by the state of charge (SOC) plays a vital role in battery protection strategies represented by battery management systems (BMSs). ...
... However, the OCV method is suitable for low-current applications and the constructed lookup table and mathematical equation parameters should be updated to compensate for the battery aging effect. In contrast to simple methods, classification and machine learning approaches were employed to create early battery failure detection methods without utilizing the SOC in the real-time tests; however, the SOC was required in the labeling phase [7], [6]. Machine learning techniques require a considerable amount of training data, and this option is not always available. ...
... It is possible to generate model elements based on actual process. In [6], [7], supervised classification approaches were proposed, in which all observations with SOC in the range of 100%-10% were labeled in the safe region; however, the remaining observations with SOC in the range of 10%-0% were assigned to the failure region. To determine the required classes before starting the actual tests, the discussed labeling strategy based on the SOC criteria was employed in this study. ...
... Contingency management for battery systems starts with prognostics and health management (PHM), and several researchers have studied precise estimation of battery remaining useful life (RUL) computed from battery state of charge (SoC). Such methods have used Extended Kalman Filter (EKF) [10], Unscented Kalman Filter (UKF) [11], unscented transform [12], particle filter [13], neural network [14], and Gaussian Process Regression (GPR) [15,16] formulations. Sharma et al. [17] proposed multi-battery reconfiguration for UAS using a prognostics-informed Markov Decision Process (MDP). ...
Unmanned aircraft systems (UAS) are being increasingly adopted for various applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper proposes risk-aware contingency management autonomy to prevent an accident in the event of component malfunction, specifically propulsion unit failure and/or battery degradation. The proposed autonomy is modeled as a Markov Decision Process (MDP) whose solution is a contingency management policy that appropriately executes emergency landing, flight termination or continuation of planned flight actions. Motivated by the potential for errors in fault/failure indicators, partial observability of the MDP state space is investigated. The performance of optimal policies is analyzed over varying observability conditions in a high-fidelity simulator. Results indicate that both partially observable MDP (POMDP) and maximum a posteriori MDP policies performed similarly over different state observability criteria, given the nearly deterministic state transition model.
... Consequently, the estimated SOC has been utilized by battery management systems (BMSs) to ensure adequate performance of the battery unit [6]. Therefore, a battery model plays a vital role in BMS structure to protect the battery unit from abuse factors, such as an over-discharge scenario [7,8,9,10]. Moreover, battery models are important tools in charging and battery lifetime-extension algorithms [11,12]. ...
... As a result, the mean values of the individual parameter arrays were determined to obtain the required constant parameter values that were utilized by the model elements in Eqs. (6)- (9). ...
In this study, the theoretical framework of optimal control was employed to construct an optimal parameter estimation (OPE) methodology that estimates the parameters of an equivalent-circuit battery model backward in time. In contrast, a forward adaptive parameter estimation (APE) method was utilized in the present study for comparison purposes. APE inspired this study to develop the proposed approach, because APE has been examined in previous studies and reported as a fast parameter estimation technique. Both techniques were tested and verified using simulation and experimental results, which showed that OPE exhibited a better performance than APE in terms of computational time and accuracy. Moreover, the proposed strategy was less affected by an increase in the number of samples of identification data. Therefore, the OPE approach is a reliable choice for recomputing the model parameters from time to time, especially when the battery model parameters vary with time owing to temperature and aging effects.
... In this context, machine learning algorithms may play a promising role. Different supervised [54][55][56][57], unsupervised [58], and reinforcement learning [59] strategies have already been addressed in the fields of individual battery system components. Yu [60] and Li et al. [61] propose prognostic state-of-health frameworks using Gaussian Process Regression (GPR). ...
In the development of battery systems for electric vehicles (EV), numerous components from different physical subareas must be harmonized with each other. Automotive engineers already use extensive simulation models to optimize individual components satisfying increasing demands of EV range and power. Yet, strategies for the combined optimization of battery systems have not been addressed in the literature. This work presents an optimization strategy for the holistic design of battery systems, which utilizes coupled simulations of technical submodels representing cellmodules, mechanics, cooling, and electronics. Given user-specified battery system requirements, methods of Gaussian Process Regression and Classification are combined to determine the optimal battery system design in terms of costs and feasibility. An inherited mixed-integer problem is addressed by using discretization of the solution space and refinement strategies in likely optimal regions. Moreover, the information gain per iteration is maximized by means of predictive calculations and parallelization methods. Testing the presented optimization strategy in different scenarios gives promising results, showcasing its robustness towards different technical requirements for battery systems. Also, exemplary analyses regarding the impact of the total installation space on costs and feasibility are conducted.
... The battery model was utilized without linearization in the UKF and PF algorithms. Battery models have a key function in battery management systems (BMSs) to ensure safe and reliable performance, as reported in [11] and [12]. Unlike BMSs, a battery model is also required in algorithms for charging control [13]. ...
... According to [37], the ML function behaves as a Nussbaum VOLUME 8, 2020 function if a ∈ (2, 3] and b = 1. Hence, the ML function is represented by Eq. (12), where (z + 1) = z (z), z > 0 is the standard gamma function. In [38], the Mittag-Leffler function was constructed in the MATLAB/Simulink environment. ...
In this study, a vehicle localization technique was employed to determine the required quantities in the identification of battery models by considering the behavior of multiple batteries instead of data from a single battery. In previous studies, a plant (e.g., a battery, motor, super-capacitor, or fuel cell) was identified based on a single piece of data. However, such an approach is disadvantageous in that it neglects the effect of process and measurement noise and assumes that the parameters obtained using data from a single plant are identical for all plants of the same type. First, deterministic parameter estimation (DPE), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) were initially applied to estimate the battery model parameters using data from a single battery. Second, a fusion-based approach was used to address the process and measurement noise problems through an adaptive unscented Kalman filter algorithm. With this approach, maximum likelihood estimation was employed to fuse multiple-battery data streams to enable the DPE, PSO, and TLBO to recalculate the model parameters based on filtered and fused quantities. A comparison between the experimental results and model outputs obtained using the aforementioned methods for parameter estimation indicated that the proposed multiple-battery approach enhances the accuracy of several identification methods. In contrast, it requires a high computational effort.
Lithium battery is an important power source of an electrical vehicle (EV). Practically, when a battery is about to fail, it needs to be removed from the load because keeping it connected can lead to permanent damage. Hence, it is important to detect battery failure to sustain the lifespan of the battery and avoid safety issues which will contribute to the sustainability of the EV. However, determining the effective disconnecting or discharging moment of the battery remains a challenging issue in EV applications. In order to solve the abovementioned problem, a control framework and maximum likelihood estimation are proposed to estimate the parameters of a battery model. In addition, both the sparse representation and dynamic mode decomposition (DMD) approaches are applied to protect the physical battery unit from a failure scenario. For comparison purposes, the proposed framework is compared to a Lyapunov-based detection approach along with neural network (NN) and linear discriminant analysis (LDA). Moreover, several state estimation algorithms are applied to estimate battery state of charge (SOC) at each time step: extended Kalman filter, extended Kalman smooth variable structure filter, and cubature Kalman filter. Finally, the experimental results showed that the proposed DMD approach outperforms the sparse representation, NN, LDA, and Lyapunov-based approaches in terms of detection accuracy. Moreover, the proposed DMD strategy is more robust towards the inaccuracy of the estimated SOC. Besides that, the proposed battery model parameter estimation approach exhibited fast processing time; therefore, it is a reliable choice for recomputing the model parameters from time to time to compensate aging effect. An autonomous unmanned aerial vehicle (UAV) was simulated as a proof-of-concept in a MATLAB environment to verify the detection performance of the proposed methods. Both actual and UAV results showed that the DMD demonstrated more robust detection performance than other methods in terms of processing time and detection accuracy.
In this study, image feature extraction techniques based on principal component analysis (PCA) and QR factorization were employed to represent the measured terminal voltage of several batteries in terms of a few dominant features instead of raw data. In previous studies, directly measured terminal voltage was used to create the feature space by inputting raw sampled data to a supervised classifier. This was ineffective because it leads to false alarms owing to data overlap and requires a relatively considerable processing time. However, the proposed methods are useful for building a reduced and distinguishable feature space, where a classifier can quickly process and separate regions with small labeling errors. Therefore, different supervised machine learning classifiers, namely neural networks (NNs), k-nearest neighbors (kNNs), and support vector machines (SVMs), were trained based on the dominant features to distinguish between the safe and failure regions of battery units. Interestingly, the proposed technique requires no knowledge of the model parameters and state of charge (SOC) in the training and testing phases, although the SOC is necessary in the data labeling stage. The results showed that the nonlinear classifiers represented by NNs and kNNs demonstrated slightly better detection performance than the linear SVMs.
Battery operated devices are common in everyday life. Deciding when a warning for impending battery voltage collapse should be triggered is not trivial. This paper develops an impending battery terminal voltage collapse detection system using a universal adaptive stabilizer (UAS) and a well-known trend filter. This eliminates requiring knowledge of battery model parameter values, or initial state-of-charge (SOC). The proposed approach overcomes the need for extensive training when compared to using neural-networks based techniques. Also, the developed trend filter when used with a UAS, eliminates the need for selecting windows of data to be processed. This is advantageous compared to earlier work, which uses a different trend filtering mechanism, because selection of window sizes is not straightforward. Further, the approach used in this work shows that the UAS based technique is implementable on a cell phone. Associated mathematical results, and experimental data from such an implementation are presented. Additionally, the technique is also applied to other larger capacity Li-ion batteries showing its versatility. The developed technique can also be used to detect when the state-of-health (SOH) of a Li-ion battery is about to enter an unsafe region.
