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Schematic illustration of simplified modular-based lithium-ion battery pack including HV/LV interfaces, LIBs, electric and electronic components
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This study provides an overview of available techniques for on-board State-of-Available-Power (SoAP)
prediction of lithium-ion batteries (LIBs) in electric vehicles. Different approaches dealing with the onboard
estimation of battery State-of-Charge (SoC) or State-of-Health (SoH) have been extensively discussed
in various researches in the past. Ho...
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Citations
... Lithium-ion batteries (LIBs) are used in portable devices, stationary battery energy storage systems, and battery electric vehicles. Accurate knowledge of the current state of charge is essential for safe and efficient operation [1][2][3] . In the battery management system the state-of-charge (SOC) is often determined by Coulomb counting, i.e., by measuring and integrating the current at the terminals of the battery. ...
The open circuit voltage hysteresis of lithium-ion batteries is a phenomenon that, despite intensive research, is still not fully understood. However, it must be taken into account for accurate state-of-charge estimation in battery management systems. Mechanistic models of the open circuit voltage hysteresis previously published are not suitable for deployment in a battery management system. Phenomenological models on the other hand can only superficially represent the processes taking place. To address this limitation, we propose a probability distributed equivalent circuit model motivated by the physical insights into hysteresis. The model incorporates hysteresis effects that are often disregarded for state estimation, while keeping the computational cost low. Although the parameterization is more demanding, the model has the advantage of providing insight into the internal state of the battery and intrinsically incorporating the effect of path-dependent rate capability.
... The outcome of this test is a power capability characteristic map for each ageing condition, where landing power, landing duration, and temperature are used as inputs to estimate the lowest SoC allowed for a successful landing of the aircraft. Characteristic maps have been O. Hatherall et al. [55]. ...
... The disadvantages of characteristic map approaches often include large data storage requirements and the large numbers of tests required to map all possible use-case scenarios [57]. Additionally, these maps are based on steadystates, so they have an inability to consider the effects of battery non-linearity [55]. Hence, model-based methods have been previously implemented to improve power capability estimation [57,58]. ...
... Generation Vehicles, to standardize the testing procedure for battery instantaneous peak power (also known as instantaneous SOP) [22]. Building on this, early studies investigated the characteristics of battery instantaneous SOP across the operation ranges of SOC, temperature, and aging level [23]. Despite ease of implementation, instantaneous SOP estimation enables limited contributions to optimize battery energy and power management, owing to its short prediction window of only one sampling interval. ...
... While considerable efforts have been directed towards integrating battery SOP into joint or co-estimation frameworks, relevant research often treated it as an extension of studies on other internal states like SOC, SOE, and SOH. Previous works in [2,23] have reviewed those SOP-involved frameworks from perspectives of battery modelling, parameter identification and state estimation, however, these studies did not offer a systematic breakdown of all key aspects with their recent advancements. ...
... While the definition of SOP is straightforward, it does not specify the way to discharge or charge batteries during a prediction window. Due to the inherent battery characteristic, it is possible to keep only one parameter-either current, voltage or power-constant to maximize the peak power capability [23,63]. Clearly, this nature can induce varied electrical behaviors of batteries functioning under different POMs, leading to diverse peak power performances and SOP estimation outcomes. ...
Accurate state of power (SOP) estimation is of great importance for lithium-ion batteries in safety-critical and power-intensive applications for electric vehicles. This review article delves deeply into the entire development flow of current SOP estimation technology, offering a systematic breakdown of all key aspects with their recent advancements. First, we review the design of battery safe operation area, summarizing diverse limitation factors and furnishing a profound comprehension of battery safety across a broad operational scale. Second, we illustrate the unique discharge and charge characteristics of various peak operation modes, such as constant current, constant voltage, constant current-constant voltage, and constant power, and explore their impacts on battery peak power performance. Third, we extensively survey the aspects of battery modelling and algorithm development in current SOP estimation technology, highlighting their technical contributions and specific considerations. Fourth, we present an in-depth dissection of all error sources to unveil their propagation pathways, providing insightful analysis into how each type of error impacts the SOP estimation performance. Finally, the technical challenges and complexities inherent in this field of research are addressed, suggesting potential directions for future development. Our goal is to inspire further efforts towards developing more accurate and intelligent SOP estimation technology for next-generation battery management systems
... In this study, IR t is predefined for each SoH and SoC based on the model parameter tables. In a real BMS application, the IR should be estimated based on available methods [37]. ...
As the Electric Vehicle market grows, understanding the implications of battery degradation on the driving experience is key to fostering trust among users and improving End of Life estimations. This study analyses various road types, charging behaviours and Electric Vehicle models to evaluate the impact of degradation on the performance. Key indicators related to the speed, acceleration, driving times and regenerative capabilities are obtained for different degradation levels to quantify the performance decay. Results show that the impact is highly dependent on the road type and nominal battery capacity. Vehicles with long and medium ranges show a robust performance for common driving conditions. Short-range vehicles perform adequately in urban and rural road conditions, but on highways, speed and acceleration reductions of up to 6.7 km/h and 3.96 (km/h)/s have been observed. The results of this study suggest that degradation should not be a concern for standard driving conditions and mid- and long-range vehicles currently dominate the market. In addition, the results are used to define a functional End of Life criterion based on performance loss, beyond the oversimplified 70–80% State-of-Health threshold, which does not consider individual requirements.
... While the definition of SOP is straightforward, it does not specify the way to discharge or charge batteries during a prediction window. Due to the inherent battery characteristic, it is possible to keep only one parameter-either current, voltage or power-constant to maximize the peak power capability [8]. Clearly, this nature induces four commonly used peak operation models (POMs), referred to as constant current (CC), constant voltage (CV), CC-CV, and constant power (CP) [9]. ...
The peak power capability of lithium-ion batteries (LIBs), or so-called state of power (SOP), plays a decisive role for electric vehicles to fulfill a specific power-intensive task. Generally, battery SOP can be achieved based on different peak operation modes (POMs), including constant current, constant voltage, constant current and constant voltage or constant power, throughout a prediction window. However, the impact of these POMs on battery peak performance and their interrelationship remain unclear by far. In light of this, we conduct a comparative study to fill this blank. Four key indices, including maximum and minimum instant magnitudes, time-averaged magnitude and falling/rising rate, are adopted to evaluate battery peak performance under each POM. Potential factors, such as load profile, length of the prediction window and battery chemistry, are considered in the comparisons. The results offer valuable insights into the distinct attributes of these POMs and their region-dependent interrelationship. Index Terms-Batteries, state of power, peak operational mode, comparative study, performance analysis. NOMENCLATURE 0 R Ohmic resistance 1 R Charge transfer resistance 1 C Charge transfer capacitance 2 R Diffusion resistance 2 C Diffusion capacitance n C Battery capacity 1 p V Charge transfer overpotential 2 p V Diffusion overpotential oc V Open circuit voltage t V Battery terminal voltage L
... Electric vehicles are widely recognized as a crucial solution to combat environmental pollution and tackle energy shortages. With the ability to use various sources of energy and multiple modes, EVs can conserve energy and improve the environment [1][2][3]. Lithium-ion batteries are widely used as power sources in EVs due to their efficiency. However, their performance degrades over time due to aging, which reduces their energy storage and power delivery capacity. ...
Significant improvements in battery performance, cost reduction, and energy density have been made since the advancements of lithium-ion batteries. These advancements have accelerated the development of electric vehicles (EVs). The safety and effectiveness of EVs depend on accurate measurement and prediction of the state of health (SOH) of lithium-ion batteries; however, this process is uncertain. In this study, our primary goal is to enhance the accuracy of SOH estimation by reducing uncertainties in state of charge (SOC) estimation and measurements. To achieve this, we propose a novel method that utilizes the gradient based optimizer (GBO) to evaluate the SOH of lithium batteries. The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory fading forgetting factor. We evaluated our method against four robust algorithms, namely particle swarm optimization-least square support vector regression (PSO-LSSV), BCRLS-multiple weighted dual extended Kalman filtering (BCRLS-MWDEKF), Total least square (TLS), and approximate weighted total least squares (AWTLS) in hybrid electric vehicle (HEV) and electric vehicle (EV) applications. Our method consistently outperformed the alternatives, with the GBO achieving the lowest maximum error. In EV scenarios, GBO exhibited maximum errors ranging from 0.65% to 1.57% and mean errors ranging from 0.21% to 0.57%. Similarly, in HEV scenarios, GBO demonstrated maximum errors ranging from 0.81% to 3.21% and mean errors ranging from 0.39% to 1.03%. Furthermore, our method showcased superior predictive performance, with low values for mean squared error (
... The constraints that can be used to calculate the power limits include the current, voltage, SOC, and temperature. A comparative study on different ECM-based power capability estimation methods can be found in [11]. ...
... To consider the influence of u on y 2 , we propagate the model (11) to yield one-step forward time-shift, ...
The power capability of a lithium-ion battery signifies its capacity to continuously supply or absorb energy within a given time period. For an electrified vehicle, knowing this information is critical to determining control strategies such as acceleration, power split, and regenerative braking. Unfortunately, such an indicator cannot be directly measured and is usually challenging to be inferred for today's high-energy type of batteries with thicker electrodes. In this work, we propose a novel physics-based battery power capability estimation method to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power, but also the internal constraints on lithium plating and thermal runaway, can be readily taken into account. The online estimation of maximum power is accomplished by formulating and solving a constrained nonlinear optimization problem. Due to the relatively high system order, high model nonlinearity, and long prediction horizon, a scheme based on multistep nonlinear model predictive control is found to be computationally affordable and accurate.
... Electric vehicles are widely recognized as a crucial solution to combat environmental pollution and 43 tackle energy shortages. With the ability to use various sources of energy and multiple modes, EVs can 44 RESEARCH ARTICLE conserve energy and improve the environment [1][2][3]. Lithium-ion batteries are widely used as power sources 45 in EVs due to their efficiency. However, their performance degrades over time due to aging, which reduces 46 their energy storage and power delivery capacity. ...
22 Significant improvements in battery performance, cost reduction, and energy density have been made since the ad-23 vancements of lithium-ion batteries. These advancements have accelerated the development of electric vehicles (EVs). 24 The safety and effectiveness of EVs depend on accurate measurement and prediction of the state of health (SOH) of 25 lithium-ion batteries; however, this process is uncertain. In this study, our primary goal is to enhance the accuracy of 26 SOH estimation by reducing uncertainties in state of charge (SOC) estimation and measurements. To achieve this, we 27 propose a novel method that utilizes the gradient-based optimizer (GBO) to evaluate the SOH of lithium batteries. The 28 GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading 29 forgetting factor. We evaluated our method against four robust algorithms, namely particle swarm optimization-least 30 square support vector regression (PSO-LSSV), BCRLS-multiple weighted dual extended Kalman filtering (BCRLS-31 MWDEKF), Total least square (TLS), and approximate weighted total least squares (AWTLS) in hybrid electric vehicle 32 (HEV) and electric vehicle (EV) applications. Our method consistently outperformed the alternatives, with the GBO 33 achieving the lowest maximum error. In EV scenarios, GBO exhibited maximum errors ranging from 0.65% to 1.57% 34 and mean errors ranging from 0.21% to 0.57%. Similarly, in HEV scenarios, GBO demonstrated maximum errors 35 ranging from 0.81% to 3.21% and mean errors ranging from 0.39% to 1.03%. Furthermore, our method showcased 36 superior predictive performance, with low values for mean squared error (MSE) (<1.8130e-04), root mean squared 37 error (RMSE) (<1.35%), and mean absolute percentage error (MAPE) (<1.4). 38
... The internal resistance-based method reflects battery health by measuring the growth rate of the battery's ohmic resistance. The powerbased method characterizes battery health by assessing the power state during charge and discharge at specific state-of-charge (SOC) levels [12,13]. ...
The state-of-health (SOH) of lithium-ion batteries has a significant impact on the safety and reliability of electric vehicles. However, existing research on battery SOH estimation mainly relies on laboratory battery data and does not take into account the multi-faceted nature of battery aging, which limits the comprehensive and effective evaluation and prediction of battery health in real-world applications. To address these limitations, this study utilizes real electric vehicle operational data to propose a comprehensive battery health evaluation indicator and a deep learning predictive model. In this study, the battery capacity, ohmic resistance, and maximum output power were initially extracted as individual health indicators from actual vehicle operation data. Subsequently, a methodology that combines the improved criteria importance through inter-criteria correlation (CRITIC) weighting method with the grey relational analysis (GRA) method is employed to construct the comprehensive battery health evaluation indicator. Finally, a prediction model based on the attention mechanism and the bidirectional gated recurrent unit (Att-BiGRU) is proposed to forecast the comprehensive evaluation indicator. Experimental results using real-world vehicle data demonstrate that the proposed comprehensive health indicator can provide a thorough representation of the battery health state. Furthermore, the Att-BiGRU prediction model outperforms traditional machine learning models in terms of prediction accuracy.
... The amount of power a battery is capable of providing to or drawing from a device across a time horizon is known as the state of power (SOP) [186]. It is a measure of the battery's instantaneous power output capability and is affected by various factors such as the battery's internal resistance, temperature, and aging. ...
Electric vehicles (EVs) have had a meteoric rise in acceptance in recent decades due to mounting worries about greenhouse gas emissions, global warming, and the depletion of fossil resource supplies because of their superior efficiency and performance. EVs have now gained widespread acceptance in the automobile industry as the most viable alternative for decreasing CO2 production. The battery is an integral ingredient of electric vehicles, and the battery management system (BMS) acts as a bridge between them. The goal of this work is to give a brief review of certain key BMS technologies, including state estimation, aging characterization methodologies, and the aging process. The consequences of battery aging limit its capacity and arise whether the battery is used or not, which is a significant downside in real-world operation. That is why this paper presents a wide range of recent research on Li-ion battery aging processes, including estimations from multiple areas. Afterward, various battery state indicators are thoroughly explained. This work will assist in defining new relevant domains and constructing commercial models and play a critical role in future research in this expanding area by providing a clear picture of the present status of estimating techniques of the major state indicators of Li-ion batteries.
