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In this paper, a model fusion method (MFM) is proposed for online state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs). Firstly, a particle swarm optimization-genetic algorithm (PSO-GA) method is cooperated with a 2-RCCPE fractional-order model (FOM) to construct battery open-circuit voltage (OCV)-SOC curve, which only relies on a part of dynamic load profile without the prior knowledge of an initial SOC. Secondly, a dual extended Kalman filter (DEKF) algorithm based on a 1-RC model is employed to identify the model parameters and estimate battery SOC with the extracted OCV-SOC curve. Furthermore, battery polarization dynamics in a SOP prediction window is analyzed from two aspects: (1) self-recovery; and (2) current excitation. They are separately simulated using 2-RCCPE FOM and 1-RC model, and then integrated through a model fusion for online SOP estimation, which enables an analytical expression of battery peak charge/discharge current in a prediction window without weakening the nonlinear characteristic of FOM. Experimental results demonstrate the improved performance of the proposed MFM for online discharge SOP estimation, where the mean absolute error and root mean square error are only 0.288 W and 0.35 W, respectively, under the urban dynamometer driving schedule profile. [ABSTRACT FROM AUTHOR]

• Impact loading can cause the short-circuit of lithium-ion batteries in an earlier displacement than quasi-static loading. • A simulation model of a cylindrical battery is developed to illustrate the loading-rate dependent short-circuit mechanisms. • A short-circuit criterion is established based on the strain-rate dependent fracture behaviour of the separator. • The results can provide a design guide to enhance crashworthiness of cylindrical batteries for electric vehicles. Lithium-ion battery failures, particularly in the case of high-speed collisions in electric vehicles, have become a growing concern. This study investigates the failure mechanism of an 18650 cylindrical battery which is indicated by the occurrence of an inner short circuit at various loading rate. The voltage drop due to an internal short circuit typically occurs shortly before the maximum force is reached in quasi-static loading cases. Whereas, under dynamic loading conditions, the battery exhibits a loading-rate effect, which causes a voltage drop due to short circuits occurring at an earlier displacement. The loading-rate hardening mechanism is primarily attributed to electrolyte flux. A finite element model of an 18650 cylindrical battery is established and calibrated with the in-situ tests results. The failure location inside the jellyroll cross-section is identified with the maximum equivalent plastic strain. Under the dynamic loading, the maximum stress corresponding to the short circuiting is higher than the quasi-static counterpart. The finite element model is used to illustrate the inner short-circuit mechanisms of the batteries under different loading rates, providing a design guide for enhancing the crashworthiness of the battery components. [ABSTRACT FROM AUTHOR]

Significant variance exists in the nominal capacity of lithium ion (Li-ion) pouch cells used for commercial electric vehicle battery packs. Accurate estimation of heat generation in such cells is critical for designing battery thermal management system. However, multi-physics models describing thermal behaviour of these cells are too complex whereas other numerical models discount the effect of cell capacity on heat generation. This paper proposes a new computational model based on artificial neural network (ANN) for estimating battery heat generation rate with cell nominal capacity as one of its key inputs along with ambient temperature, discharge rate and depth of discharge. A custom-designed calorimeter is utilised for experimentally generating the training dataset for the ANN. Problem of data scarcity is addressed analytically and virtual samples are produced via enthalpy formulation for battery heat generation. Subsequently, the model is trained using Levenberg–Marquardt algorithm. Results disclose that a three-layered feedforward ANN with one hidden layer having six neurons is optimum for this application. The architecture of the trained ANN for accurately simulating thermal behaviour of LiFePO 4 pouch cells of the nominal capacities from 8 to 20 Ah under varied conditions is exemplified. [ABSTRACT FROM AUTHOR]

A lithium iron phosphate battery (LiFePO 4 ) pack is one of the main power resources for electric vehicles and the non-uniformity of cells in the battery pack has become the bottleneck to improve the pack capacity. An active balancing method based on chargeable and dischargeable capacities, derived from the dynamically estimated state of charge (SOC) and capacity in the pack, is proposed to tackle this problem in both the charging and discharging processes. To determine the current of each cell in balancing operation, one extra current sensor is added with a chosen flyback balancing circuit. The balancing simulation of a LiFePO 4 battery pack has been conducted in the moderate and severe capacity imbalance scenarios. The simulation results show that the proposed battery balancing method has better performance than the other balancing methods based on voltage or SOC in increasing the charged and discharged pack capacity in the charging and discharging process. [ABSTRACT FROM AUTHOR]

This paper presents a robust sliding-mode observer (RSMO) for state-of-charge (SOC) estimation of a lithium-polymer battery (LiPB) in electric vehicles (EVs). A radial basis function (RBF) neural network (NN) is employed to adaptively learn an upper bound of system uncertainty. The switching gain of the RSMO is adjusted based on the learned upper bound to achieve asymptotic error convergence of the SOC estimation. A battery equivalent circuit model (BECM) is constructed for battery modeling, and its BECM is identified in real time by using a forgetting-factor recursive least squares (FFRLS) algorithm. The experiments under the discharge current profiles based on EV driving cycles are conducted on the LiPB to validate the effectiveness and accuracy of the proposed framework for the SOC estimation. [ABSTRACT FROM AUTHOR]

A theoretical electrochemical thermal model combined with a thermal resistive network is proposed to investigate thermal behaviours of a battery pack. The combined model is used to study heat generation and heat dissipation as well as their influences on the temperatures of the battery pack with and without a fan under constant current discharge and variable current discharge based on electric vehicle (EV) driving cycles. The comparison results indicate that the proposed model improves the accuracy in the temperature predication of the battery pack by 2.6 times. Furthermore, a large battery pack with four of the investigated battery packs in series is simulated in the presence of different ambient temperatures. The simulation results show that the temperature of the large battery pack at the end of EV driving cycles can reach to 50 °C or 60 °C in high ambient temperatures. Therefore, thermal management system in EVs is required to maintain the battery pack within the safe temperature range. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel approach for battery state of charge (SOC) estimation in electric vehicles (EVs) based on an adaptive switching gain sliding mode observer (ASGSMO) has been presented. To design the ASGSMO for the SOC estimation, the state equations based on a battery equivalent circuit model (BECM) are derived to represent dynamic behaviours of a battery. Comparing with a conventional sliding mode observer, the ASGSMO has a capability of minimising chattering levels in the SOC estimation by using the self-adjusted switching gain while maintaining the characteristics of being able to compensate modelling errors caused by the parameter variations of the BECM. Lyapunov stability theory is adopted to prove the error convergence of the ASGSMO for the SOC estimation. The lithium-polymer battery (LiPB) is utilised to conduct experiments for determining the parameters of the BECM and verifying the effectiveness of the proposed ASGSMO in various discharge current profiles including EV driving conditions in both city and suburban. [Copyright &y& Elsevier]

Accurate and rapid fault detection is essential for the safe operation of lithium-ion batteries in electric vehicles. However, conventional fault detection methods dependent on constant thresholds may have false alarms or missing alarms due to the inevitable disturbances resulted from the battery system modeling errors and measurement noises. In this paper, we design a multi-objective nonlinear fault detection observer for lithium-ion batteries, which is robust against disturbances but sensitive to battery multi-fault. We then perform formal stability and L ∞ / H _ performance analysis for the resultant estimation error system. Furthermore, tractable design procedures for the observer gain parameter and an adaptive threshold are derived. Then, via adaptive thresholding, a delicate three-step multi-fault detection scheme is developed to detect the occurrence of battery various faults, including short-circuit faults, current and voltage sensor faults. Finally, the efficacy of the proposed scheme is validated under several experimental case studies involving a variety of faults with their different levels of severity and erroneous SOC initialization. • A general battery model that incorporates both multi-faults and disturbances. • A multi-objective nonlinear observer with formal stability and performance analysis. • A three-step multi-fault detection scheme with an adaptive threshold. • Extensive experimental studies involving a variety of faults. [ABSTRACT FROM AUTHOR]

Parasitic load, which describes electrical energy consumed by battery thermal management system (TMS), is an important design criterion for battery packs. Passive TMSs using phase change materials (PCMs) are thus generating much interest. However, PCMs suffer from low thermal conductivities. Most current thermal conductivity enhancement techniques involve addition of foreign particles to PCMs. Adding foreign particles increases effective thermal conductivity of PCM-systems but at expense of their latent heat capacity. This paper presents an alternate approach for improving thermal performance of PCM-based TMSs. The introduced technique involves placing battery cells in a vertically inverted position within the battery-pack. It is demonstrated through experiments that inverted cell-layout facilitates build-up of convection current in the pack, which in turn minimises thermal variations within the PCM matrix by enabling PCM mass transfer between the top and the bottom regions of the battery pack. The proposed system is found capable of maintaining tight control over battery cell temperature even during abusive usage, defined as high-rate repetitive cycling with minimal rest periods. In addition, this novel TMS can recover waste heat from PCM-matrix through thermoelectric devices, thereby resulting in a negative parasitic load for TMS. [ABSTRACT FROM AUTHOR]

Accurate state-of-charge (SoC) estimation is remarkably difficult due to nonlinear characteristics of batteries and complex application environment in electric vehicles (EVs), particularly low temperature and low SoC. In this paper, an improved battery model is first built using a feedforward neural network (FFNN) by introducing newly defined inputs. Based on the FFNN model and the extended Kalman filter algorithm, a FFNN-based SoC estimation method is designed, and its robustness is verified and discussed using the experimental data obtained at different temperatures. Finally, a hardware-in-loop test bench is built to further evaluate the real-time and generalization of the designed FFNN model. The results show that the SoC estimation can converge to the reference value at erroneous settings of an initial SoC error and an initial capacity error, and the SoC estimation errors can be stabilized within 2% after convergence, which applies to all the cases discussed in this paper, including low temperature and low SoC. This indicates that the FFNN-based method is an effective method to estimate SoC accurately in complex EV application environment. • Battery model is built using a feedforward neural network with newly defined inputs. • The SoC estimation method performs well even at low SoC and low temperature. • The proposed method can result in a good accuracy even using an inaccurate capacity. • The effectiveness of the method is verified by hardware-in-loop test. [ABSTRACT FROM AUTHOR]

• A model for SOH estimation and degradation diagnosis is present. • Proposing a method to select appropriate segments of charging data as model input. • This model development process does not rely on aging data. • The method is validated on real aging data. Lithium-ion batteries (LiBs) have been widely used in electric vehicles and portable electronics. However, the performance and safety of these applications are highly dependent on degradation of LiBs. In this paper, three contributions have been made to achieve reliable degradation diagnosis and State-of-Health (SOH) estimation: (1) Open-circuit voltage is reconstructed to diagnose degradation modes of LiBs by performing scaling and translation transformations on open-circuit potential curves. (2) A degradation diagnosis model is developed to quantify aging characteristics of LiBs. In this model, a segment of charging data is taken to estimate SOH and the degradation modes in a degradation path. (3) An appropriate voltage range of the charging data is selected to improve model estimation accuracy. Experimental results show that the proposed method can achieve reliable degradation diagnosis and accurate SOH estimation with the maximum error of 1.44%. [Display omitted] [ABSTRACT FROM AUTHOR]

Battery models are the cornerstone to battery state of charge (SOC) estimation and battery management systems in electric vehicles. This paper proposes a novel fractional-order model for a battery, which considers both Butler–Volmer equation and fractional calculus of constant phase element. The structure characteristics of the proposed model are then analyzed, and a novel identification method, which combines least squares and nonlinear optimization algorithm, is proposed. The method is proven to be efficient and accurate. Based on the proposed model, a fractional-order unscented Kalman filter is developed to estimate SOC, while singular value decomposition is applied to tackle the nonlinearity of Butler–Volmer equation and fractional calculus of constant phase element. The systematic comparison between the proposed model and traditional fractional order model is carried out on two LiNiMnCo lithium-ion batteries at different temperatures, ageing levels, and electric vehicle current profiles. The comparison results show that the proposed model has higher estimation accuracy in battery terminal voltage and SOC than the traditional model over wide range of temperature and ageing level under electric vehicle operation conditions. Furthermore, the hardware-in-the-loop test validates that the proposed SOC estimation method is suitable for SOC estimation in electric vehicles. [ABSTRACT FROM AUTHOR]

A bidirectional (Bi) DC/DC converter is one of the key components in a hybrid energy storage system for electric vehicles and plug-in electric vehicles. Based on the detailed analysis of the losses in the converter, this paper firstly develops a model to theoretically calculate the efficiency of the converter. Then, the influences of temperature, switching frequency, duty cycle and material of switching device on the converter’s efficiency are experimentally investigated. The analysis of the experimental results has shown that (1) The efficiency at the switching frequency of 15 kHz is about 2% higher than that of 25 kHz. (2) The efficiency at 25 °C is similar to that at 85 °C for the MOSFET SiC while the efficiency at 25 °C is 2% higher than that at 85 °C for the IGBT Si for both buck and boost modes. (3) In buck mode, when the duty cycles are decreasing from 66.7%, 50% to 33.33%, the peak efficiencies are also decreasing from 97.6%, 94.5% to 90.3%, respectively. In boost mode, when the duty cycle is increasing from 33.33%, 50% to 75%, the peak efficiency is decreasing from 96.9%, 96.5% to 92.4%, respectively. (4) The developed model can calculate the converter’s efficiency accurately [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity and complex time-variant property of batteries, the existing state of charge (SOC) estimation approaches based on a single equivalent circuit model (ECM) cannot provide the accurate SOC for the entire discharging period. This paper aims to present a novel SOC estimation approach based on a multiple ECMs fusion method for improving the practical application performance. In the proposed approach, three battery ECMs, namely the Thevenin model, the double polarization model and the 3rd order RC model, are selected to describe the dynamic voltage of lithium-ion batteries and the genetic algorithm is then used to determine the model parameters. The linear matrix inequality-based H-infinity technique is employed to estimate the SOC from the three models and the Bayes theorem-based probability method is employed to determine the optimal weights for synthesizing the SOCs estimated from the three models. Two types of lithium-ion batteries are used to verify the feasibility and robustness of the proposed approach. The results indicate that the proposed approach can improve the accuracy and reliability of the SOC estimation against uncertain battery materials and inaccurate initial states. [ABSTRACT FROM AUTHOR]

• An improved model with the voltage as input and current as output (VICO) is proposed. • The established VICO model is extended to an n -order VICO model. • The fault diagnosis method of current sensor is realized with the first-order VICO model. • The adaptability under different operating conditions and merit in detecting time are verified. Battery management systems (BMSs) are very important to ensure the safety of electric vehicles. The normal operation of BMSs is highly dependent on the accuracy of battery sensors. The present fault diagnosis efficiency of current sensors is much lower than that of voltage sensors due to model limitations in conventional methods. In this paper, a fault diagnosis method based on an improved model with voltage as input and current as output (VICO) is proposed to detect current sensor faults, where the least squares method combined with the unscented Kalman filter is used to estimate the fault current of current sensor. By comparing the estimated fault current with the diagnosis threshold, the fast fault diagnosis of current sensor is realized. The proposed method is verified under different operating conditions and compared with the methods based on state of charge and open-circuit voltage residuals. To highlight the importance of the proposed method, the influence and possible causes of minor faults and temperature on diagnosis are analyzed. The experimental results show that the method can detect the fault of the current sensor more accurately and quickly compared with the conventional methods, and has the ability to detect minor faults and adaptability under different operating conditions and temperatures. [ABSTRACT FROM AUTHOR]

• ESC behaviors at various temperatures are investigated experimentally. • ESC-induced heat generation and its impacts on electrical behaviors is modeled. • Distribution and anisotropy diffusion of ESC-caused heat generation is delineated. • An electro-thermal coupling model is proposed for batteries under ESCs. • Effectiveness of the proposed model is verified by experimental data. External short circuit (ESC) fault, which can cause large current and high temperature, is one of the main reasons for battery failure. Its analysis and diagnosis remains a challenging task due to complex electro-thermal characteristics of batteries under ESCs. In this paper, ESC experiments at various temperatures are conducted to investigate the impact of temperature on battery electro-thermal behaviors. Based on the analysis of the experimental data, heat generation inside a battery caused by ESC-induced high current and side reactions is modeled. The heat distribution and diffusion are also modeled by considering battery's internal jellyroll structure. The combination of the heat generation, distribution and diffusion models forms a novel electro-thermal coupling model, which is used to predict the complex thermal and electrical properties of a battery under ESCs. The presented model is simulated and verified by the test data. The maximum root mean square error of ESC current prediction is less than 1.73A and the maximum errors of the internal temperatures and the surface temperatures are only 1.771% and 3.915%, respectively. These results verify the effectivceness of the presented model. It is expected that the presented model is useful for safety analysis, temperature prediction and fault diagnosis applications of the lithium-ion batteries under ESC. [ABSTRACT FROM AUTHOR]

Current studies on the mechanical abuse of lithium-ion batteries usually focus on the mechanical damage process of batteries inside a jelly roll. In contrast, this paper investigates the internal short circuits inside batteries. Experimental results of voltage and temperature responses of lithium-ion batteries showed that battery internal short circuits evolve from a soft internal short circuit to a hard internal short circuit, as battery deformation continues. We utilized an improved coupled electrochemical-electric-thermal model to further analyze the battery thermal responses under different conditions of internal short circuit. Experimental and simulation results indicated that the state of charge of Li-ion batteries is a critical factor in determining the intensities of the soft short-circuit response and hard short-circuit response, especially when the resistance of the internal short circuit decreases to a substantially low level. Simulation results further revealed that the material properties of the short circuit object have a significant impact on the thermal responses and that an appropriate increase in the adhesion strength between the aluminum current collector and the positive electrode can improve battery safety under mechanical abusive conditions. [ABSTRACT FROM AUTHOR]