Federal Urban Driving Schedule Research Articles

Efficient State of Charge Estimation of Lithium-Ion Batteries in Electric Vehicles using Evolutionary Intelligence-assisted GLA-CNN-Bi-LSTM Deep Learning Model

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN-Bi-LSTM network meticulously. This approach not only refines the model’s accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model’s robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1%. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust and swiftly accurate estimation of the state of charge (SOC) of lithium-iron-phosphate batteries during electric vehicle duty cycles. The established simplified hysteresis model in this paper significantly enhances the fitting accuracy during charging and discharging processes, compensating for voltage deviations induced by hysteresis characteristics. The SOC estimation, even in the face of model parameter changes under complex working conditions during electric vehicle duty cycles, maintains high robustness by capitalizing on the easy convergence and parameter insensitivity of ASMO. Lastly, experiments conducted under different temperatures and FUDS and DST conditions validate that the SOC estimation of lithium-iron-phosphate batteries, based on the adaptive sliding-mode observer and the simplified hysteresis model, exhibits enhanced robustness and faster convergence under complex working conditions and temperature variations during electric vehicle duty cycles.

State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm

The accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the AdaBoost–BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy and poor generalization ability. The proposed model employs a back propagation neural network (BPNN) for the preliminary estimation. Subsequently, an AdaBoost–BPNN model is developed as a strong learner using the AdaBoost integration algorithm. Each BPNN sub-model serves as a weak learner within the AdaBoost framework. The final output of the strong learner is obtained by combining the individual outputs from the weak learners using weighting factors. This adaptive adjustment of weighting factors enhances the accuracy of SOC estimation. The proposed SOC estimation algorithm is evaluated and validated through experimental analysis. Throughout the paper, theoretical analysis is conducted, and the proposed AdaBoost–BPNN model is validated and verified using experimental results. The results demonstrate that the AdaBoost–BPNN model outperforms traditional methods in accurately estimating SOC under various conditions, including constant current-constant voltage (CCCV) charging, dynamical stress testing (DST), US06, a federal urban driving schedule (FUDS), and pulse discharge conditions.

A totally coupled multi time-scale framework containing full parameters online identification and SOC real-time estimation of lithium-ion battery based on a fractional order model

Fractional order model (FOM) is one of the most promising choices for lithium-ion battery simulation and internal states estimation due to its high modeling accuracy. However, online identifying the order and parameters of FOM is extremely tough, which also makes it difficult to be applied in complex and variable operating conditions. To address this issue, this paper proposes a totally coupled multi time-scale framework that contains full parameters online identification of FOM and state-of-charge (SOC) real-time estimation of lithium-ion battery. Firstly, a novel global optimization algorithm named improved particle swarm optimization is designed to update the order of FOM on macro-time scale with low computational burden. On the basis of the determined order, the rest parameters of FOM are further identified by forgetting factor recursive least square algorithm on micro-time scale. With those online identified parameters, adaptive extended Kalman filter is finally adopted to track battery SOC in every instant. The estimation accuracy, adaptability to different operating temperatures and battery aging, and robustness ability against uncertain initialization of the developed method is verified under Federal Urban Driving Schedule tests. The corresponding results demonstrate that the proposed method can realize accurate full parameters online identification of FOM and precise SOC real-time estimation against different temperatures, battery aging states and initialization. Using the developed method, the mean absolute error (MAE) and root mean square error (RMSE) of SOC estimation of the fresh battery under temperature ranging from 0 °C to 50 °C can be limited below 0.9 % and 1.1 %, respectively. Even if the SOC-OCV relationship is not timely updated, those two indexes of the aged battery achieved by the proposed method can still be controlled within 3 % under temperature ranging from 0 °C to 50 °C. Moreover, the comparison with other typical FOM-based estimation methods is also conducted, whose results indicate that the developed method has apparently superior comprehensive performance on estimation accuracy and adaptation.

Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles

State of charge (SoC) estimation is critical for the safe and efficient operation of electric vehicles (EVs). This work proposes a hybrid multi-layer deep neural network (HMDNN)-based approach for SoC estimation in EVs. This HMDNN uses Mountain Gazelle Optimizer (MGO) as a training algorithm for the deep neural network. Our method leverages the intrinsic relationship between the SoC and the voltage/current measurements of the EV battery to accurately estimate the SoC in real time. We evaluate our approach on a large dataset of real-world EV charging data and demonstrate its effectiveness in comparison to traditional SoC estimation methods. Four diverse Li-ion battery datasets of electric vehicles are employed which are the dynamic stress test (DST), Beijing dynamic stress test (BJDST), federal urban driving schedule (FUDS), and highway driving schedule (US06) with different temperatures of 0oC,25oC,45oC. The comparison is made with Mayfly Optimization Algorithm based DNN, Particle Swarm Optimization based DNN and Back-Propagation based DNN. The evaluation indices used are normalized mean square error (NMSE), root mean square error (RMSE), mean absolute error (MAE), and relative error (RE). The proposed algorithm achieves 0.1% NMSE and 0.3% RMSE on average on all datasets, which validates the effective performance of the proposed model. The results show that the proposed neural network-based approach can achieve higher accuracy and faster convergence than existing methods. This can enable more efficient EV operation and improved battery life.

A novel Kalman-filter-based battery internal temperature estimation method based on an enhanced electro-thermal coupling model

Estimating the internal temperature of lithium-ion batteries (LIBs) in battery management systems (BMS) is critical as it enables the system to monitor and manage the actual temperature of LIBs, thereby ensuring their long-term performance, safe operation and prevention of thermal runaway. However, the existing BMS can only monitor the surface temperature of the battery. To accurately estimate the internal temperature of LIBs during normal operation, this paper proposes an enhanced electro-thermal coupling model to calculate the internal temperature of LIBs. This model combines an improved second-order resistance-capacitance equivalent circuit model with a two-state thermal model to provide a more precise calculation of the internal temperature of LIBs. The model's parameters are determined using the Trust-Region Reflective based nonlinear least squares method, and the Kalman-filter algorithm is used to estimate the internal temperature of the battery based on the model. The proposed method has been demonstrated to be effective through experimental results, with a maximum root mean square error of 0.5705 °C at the battery testing conditions of Standard Charge, Federal Urban Driving Schedule (FUDS), Dynamic Stress Test (DST) and Constant Current Discharge (CC-Discharge). Due to its low cost and simplicity, this method is suitable for practical engineering applications.

State of charge estimation of lithium-ion battery based on improved adaptive boosting algorithm

State of charge (SOC) is a crucial battery state to calibrate the remaining charge of lithium-ion batteries in electric vehicles. Accurate battery SOC estimation is one of the keys and difficult issues for lithium-ion batteries. To achieve SOC estimation accurately, an improved adaptive boosting algorithm called AdaBoost.RT algorithm, which is a regression ensemble algorithm and uses the extreme learning machine (ELM) as weak learners, is applied to estimate the SOC of lithium-ion batteries in this paper. And a two-step method is also proposed to denoise experimental data, which are used as the input data of the proposed SOC estimation method, to further enhance the accuracy of SOC estimation. Firstly, a complementary ensemble empirical mode decomposition with adaptive noise method (CEEMDAN) is utilized to decompose the experimental data including battery voltage and current according to the frequency of the signals to form different frequency components. Secondly, a wavelet threshold denoising method (WTD) is combined to denoise these components. The denoised components trend to form a new battery signal by the linear superposition principle and input into the proposed SOC method in this paper. The combined SOC estimation method can be called CEEMDAN-WTD-AdaBoost-ELM (CWAELM) method. To verify the proposed method, experiments are conducted under Beijing dynamic stress test (BJDST), dynamic stress test (DST), federal urban driving schedule (FUDS), and highway driving schedule (US06) conditions at 0 °C, 25 °C and 45 °C temperatures, respectively. The results show that the proposed model has good robustness and accuracy in the SOC estimation of lithium-ion batteries under various working conditions at different temperatures.

Security-Ensured State of Charge Estimation of Lithium-Ion Batteries Subject to Malicious Attacks

This paper is concerned with the secure estimation problem of the state of charge (SOC) for Lithium-ion batteries subject to deception attacks. A second-order resistor-capacitor (RC) equivalent circuit model (ECM), whose parameters are identified by means of the extended Kalman filter algorithm, is applied to characterize the static and dynamic behaviors of a Lithium-ion battery. With the aid of the polynomial-function fitting technique, the nonlinear relationship between the open circuit voltage and SOC of the ECM is established. The deception attacks launched by the malicious adversary are taken into consideration during the data transmission from the voltage/current sensor to the estimator. By employing the Lyapunov stability theory, a second-order RC-ECM-based nonlinear observer is designed in the presence of deception attacks. In addition, sufficient conditions are derived to guarantee the uniform boundedness of the estimation error and the estimator gain matrix is implicitly parameterized by solving finite linear matrix inequalities with certain constraints. Finally, the effectiveness of the proposed estimation method is verified through the experiments under the Dynamic Stress Test and Federal Urban Driving Schedule driving cycle.

A Surface Temperature Estimation Method for Lithium-Ion Battery Using Enhanced GRU-RNN

To monitor the thermal performance of the battery, the surface temperature (ST) of the battery is normally directly measured by temperature sensors. As the number of battery cells or strings increases, the number of temperature sensors increases proportionally. This increases the cost and reduces the reliability of the battery systems. To solve this problem, this article introduces a method to accurately estimate the ST of lithium-ion batteries using a recurrent neural network (RNN) with gated recurrent unit (GRU). First, this article analyzes the battery ST distribution theory and proves that it is a time series task since the present ST is conditioned on the previous state. Second, a GRU-RNN model is adopted to estimate the battery ST as this model has the ability to automatically encode dependencies in time and accurately estimate the battery ST without using any physical battery models or filters. Third, an improved data normalization method is proposed to enhance the estimation accuracy and robustness. Fourthly, the proposed data normalization method is incorporated into the stacked GRU-RNN to estimate the battery ST from compulsory online signals. The proposed method is verified with LiFePO4 using US06 and Federal Urban Driving Schedule (FUDS) profiles under four fixed ambient temperatures and with LiNiCoAIO2 using a mixed dynamic profiles under varying ambient temperature ranges (from 10 °C to 25 °C). The estimation error using mean absolute error (MAE) is less than 0.2 °C over all the fixed ambient temperature conditions and 0.42 °C over the varying ambient temperature conditions.

Estimation of battery internal resistance using built-in self-scaling method

This paper proposes the use of the built-in self-scaling (BS) method for the effective estimation of the internal resistance of lithium-ion batteries. The internal resistance is a measure of the battery's state-of-health and an important parameter to monitor, especially in safety-critical applications such as hybrid electric vehicle applications. The BS technique works by identifying the system's impulse response and then computing the resistance from this response. This approach makes use of a prior DC gain which capitalizes on the fact that the state-of-health changes slowly with time. The BS method can be utilized on the fly in real time, is passive, and has high accuracy which is invariant with respect to the battery dynamics. Simulation results show that the BS method reduces the mean square error by factors of 32, 69 and 20 compared with the series resistance, the least squares and data pieces, and the kernel-based techniques, respectively, in the absence of hysteresis. The corresponding values in the presence of hysteresis are 42, 62 and 21, respectively. Experimental results using a lithium nickel manganese cobalt oxide battery and a dynamic current profile based on the Federal Urban Driving Schedule further confirm the superiority of the proposed BS approach.

An improved parameter identification method considering multi-timescale characteristics of lithium-ion batteries

To monitor and predict battery states, a battery model with accurate model parameters is important to battery management systems (BMS). However, for multi-timescale dynamic characteristics, the precision and adaptability of parameter identification of the Li-ion battery model is unsatisfactory up to now. In this paper, an improved parameter identification algorithm is proposed combining fixed memory recursive least squares (FMRLS) and fading extended Kalman filter (FEKF) which are used to obtain the fast dynamic (FD) and slow dynamic (SD) parameters of equivalent circuit model (ECM) respectively. Open-circuit voltage (OCV) is identified as a component of the SD part because of its slow dynamic nature in this algorithm. Federal urban driving schedule (FUDS) and dynamic stress test (DST) tests with different initial state of charge (SOC) and temperatures were employed for verifications, and the results show that the algorithm can track the battery terminal voltage in time and the root mean square error (RMSE) is as low as 1 mV. Meanwhile, the results reveal that the advanced SOC-OCV tests can be avoided indeed, and model parameters identified by this algorithm have good robustness in different temperatures and high consistency in different operating conditions which are significantly better than conventional algorithms.

Online Model Parameter and State of Charge Estimation of Li-Ion Battery Using Unscented Kalman Filter Considering Effects of Temperatures and C-Rates

This paper proposed a technique for online modeling and state of charge (SoC) estimation of Li-ion batteries considering the effects of operating temperatures and C-rates. The proposed battery model, which is updated in real-time, is incorporated with the unscented Kalman filter (UKF) for accurate and online SoC estimation. In the proposed technique, Thevenin-based equivalent circuit model of Li-ion battery is used. Each of the RC networks of the Thevenin model is represented as a first-order linear time-invariant (LTI) system. The offline RC parameters are extracted as the parameters of the first-order LTI system using the final value theorem. Then the offline RC parameters are converted to the online model parameters compensating the effects of operating temperatures and C-rates. Finally, the UKF technique is augmented with the proposed online battery model for higher accurate and online SoC estimation. An experimental setup has been developed in the LabVIEW platform to validate the proposed battery model and apply it for online SoC estimation. The proposed online battery model and UKF are developed in Matlab and then integrated with the LabVIEW program using the Matlab-LabVIEW interface called Math Script. The proposed technique has been rigorously validated in the laboratory under hybrid pulse power characterization (HPPC) and federal urban driving schedule (FUDS) tests under wide range of varying operating temperatures and C-rates. In the HPPC test, the SoC estimated using the proposed technique has a maximum absolute relative percentage error (ARPE) of less than 2%, whereas the battery model that does not account for temperature and C-rate effects has a maximum ARPE of 8.79 percent, which is 4.30 times higher than the proposed technique. The proposed approach yielded mean ARPE of 0.30%, 1.02%, and 0.81% in the FUDS test at 25 °C, 0 °C, and 40 °C, respectively, which is performed in dynamic identical C-rates. Furthermore, higher accuracy and effectiveness of the proposed technique have been validated under the presence of white and coloured measurement noises.

State of charge estimation for lithium-ion batteries under varying temperature conditions based on adaptive dual extended Kalman filter

Accurate estimation of the state-of-charge (SOC) is of extreme importance for the reliability and safety of lithium-ion battery operation, for prevention of overcharge, deep discharge, and irreversible damage to batteries. Traditional SOC estimation methods do not consider the effect of temperature on estimation, which may lead to significant errors in the SOC estimation. Considering the effect of temperature change on SOC estimation for lithium-ion batteries, this paper presents a SOC estimation method based on adaptive dual extended Kalman filter (ADEKF). First, the radial basis function neural network (RBFNN) and the forgetting factor recursive least square (FFRLS) methods are adopted to characterize the relationship between battery temperature and polarization resistance and capacitance (RC) of the dual-polarization model based on experimental lithium-ion battery data, then the relationship between identified RC parameters and temperature is described by the temperature characteristic function. Secondly, an estimation method based on the ADEKF is proposed to update the RC parameters online. This method could reduce the SOC estimation error caused by the mismatch between the set RC parameter value and the actual RC parameter value due to ambient temperature changes. Finally, the experimental data of federal urban driving schedule (FUDS) at -10°C, 25°C, and 50°C are selected to simulate and verify the SOC estimation method proposed in this paper. The results indicate that, compared with the method not considering the effect of ambient temperature, the method developed in this paper is able to achieve accurate SOC estimation with a smaller root mean square error and mean absolute error in a wide range of temperatures.

Sensitivity of Fractional-Order Recurrent Neural Network with Encoded Physics-Informed Battery Knowledge

In the field of state estimation for the lithium-ion battery (LIB), model-based methods (white box) have been developed to explain battery mechanism and data-driven methods (black box) have been designed to learn battery statistics. Both white box methods and black box methods have drawn much attention recently. As the combination of white box and black box, physics-informed machine learning has been investigated by embedding physic laws. For LIB state estimation, this work proposes a fractional-order recurrent neural network (FORNN) encoded with physics-informed battery knowledge. Three aspects of FORNN can be improved by learning certain physics-informed knowledge. Firstly, the fractional-order state feedback is achieved by introducing a fractional-order derivative in a forward propagation process. Secondly, the fractional-order constraint is constructed by a voltage partial derivative equation (PDE) deduced from the battery fractional-order model (FOM). Thirdly, both the fractional-order gradient descent (FOGD) and fractional-order gradient descent with momentum (FOGDm) methods are proposed by introducing a fractional-order gradient in the backpropagation process. For the proposed FORNN, the sensitivity of the added fractional-order parameters are analyzed by experiments under the federal urban driving schedule (FUDS) operation conditions. The experiment results demonstrate that a certain range of every fractional-order parameter can achieve better convergence speed and higher estimation accuracy. On the basis of the sensitivity analysis, the fractional-order parameter tuning rules have been concluded and listed in the discussion part to provide useful references to the parameter tuning of the proposed algorithm.

Secure Estimation Against Malicious Attacks for Lithium-Ion Batteries Under Cloud Environments

This paper is concerned with the secure estimation problem for the state of charge of Lithium-ion batteries subject to malicious attacks during the data transmission from sensors to cloud-based battery management system terminal. First, the second-order resistance-capacitance equivalent circuit model, whose parameters are identified by Kalman filter in an off-line manner, is introduced to describe the internal dynamics of lithium-ion batteries. Then, by applying the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula> detection mechanism, real-time malicious attacks are first detected and then a secure estimator is designed to suppress the influence of attacks on the estimation performance. An upper bound of the filtering error covariance is determined by solving certain coupled Riccati-like equations, and the filter parameter is obtained by minimizing such an upper bound at each time step. Finally, the validity of the proposed attack detection approach and the effectiveness of the developed estimation scheme are verified by experiment results under Federal Urban Driving Schedule condition.

Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter

Accurate state-of-charge (SOC) estimation of lithium batteries is of great significance for electric vehicles. In this paper, a combined estimation method of multi-innovation unscented Kalman filter (MIUKF) and fractional order square root cubature Kalman filter (FSRCKF) for lithium batteries is proposed. Firstly, the adaptive genetic algorithm (AGA) is applied to carry out offline parameter identification for the fractional order model (FOM) of a lithium battery under the Dynamic Stress Test (DST). Then, battery SOC is estimated by FSRCKF, while the Ohm internal resistance R0 of the fractional order battery model is estimated and updated by MIUKF in real time. The results show that MIUKF-FSRCKF is better than FSRCKF, FCKF and SRCKF in estimating the SOC of lithium batteries under the Federal Urban Driving Schedule (FUDS), Beijing Dynamic Stress Test (BJDST) and US06 Highway Driving Schedule tests, especially when R0 is inaccurate.

Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction

It is challenging for a battery management system to estimate the State-Of-Charge (SOC) of batteries. A novel model-based method, using a Dual Extended Kalman Filtering algorithm (DEKF) and Back Propagation Neural Network (BPNN), is proposed to estimate and correct lithium-ion batteries. The results of acceptable SOC estimation are achieved using the DEKF to estimate the battery SOC and simultaneously update model parameters online, while the SOC estimation error is in real-time predicted by the trained BPNN. To further reduce the SOC estimation error, the SOC estimated by the DEKF is corrected by adding the predicted estimation error. The SOC estimation results between the original DEKF and BPNN-based updated DEKF methods under the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST), the Beijing Dynamic Stress Test (BJDST) and the US06 Highway Driving Schedule are compared. Experimental results show that the SOC error reduces considerably after correcting the estimated SOC. The corrected SOC Root-Mean Square Errors (RMSEs) decrease by an average of seven times compared with the case of no correction. The constant current discharge test verifies the generality and robustness of the proposed method. The modification to the SOC estimation results using ordinary EKF under the above four sophisticated dynamic tests verifies the effectiveness of the proposed method.

Voltage sensor fault detection, isolation and estimation for lithium-ion battery used in electric vehicles via a simple and practical method

Accurate measurement information, especially precise voltage, is essential for model-based multi-state estimation algorithms of lithium-ion battery. Regarding the shortcomings in existing diagnosis methods, such as the difficulty in threshold value determination, low voltage sensor fault detection efficiency and the assumption of no multiple faults occur simultaneously, a simple and practical voltage sensor fault detection, isolation and estimation method is proposed in this paper. Firstly, the widely used algorithm by fusion of recursive least square and extend Kalman filter is adopted to obtain the measurement innovation (MI) between measured and estimated terminal voltage during online state-of-charge estimation process. Subsequently, focusing on the MI generated under faultless and faulty voltage sensors, a simple feature point (FP) identification-based voltage sensor detection and isolation method is further designed. Finally, with the identified FP's y-coordinate, a three-step based voltage sensor fault estimation method is developed to determine the fault mode and the corresponding fault value. Moreover, the proposed voltage sensor fault diagnosis method is verified by Federal Urban Driving Schedule test, whose results demonstrate that it can still realize immediate voltage sensor fault detection (at the moment of sensor fault occurrence) and accurate voltage sensor fault estimation even though there exist certain current sensor fault.

Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model

Lithium-ion battery devices are essential for energy storage and supply in distributed energy generation systems. Robust battery management systems (BMSs) must guarantee that batteries work within a safe range and avoid the damage caused by overcharge and overdischarge. The state-of-charge (SoC) of Li-ion batteries is difficult to observe after batteries are manufactured. The hysteresis phenomenon influences the existing battery modeling and SoC estimation accuracy. This research applies a terminal sliding mode observer (TSMO) algorithm based on a hysteresis resistor-capacitor (RC) equivalent circuit model to enable accurate SoC estimation. The proposed method is evaluated using two dynamic battery tests: the dynamic street test (DST) and the federal urban driving schedule (FUDS) test. The simulation results show that the proposed method achieved high estimation accuracy and fast response speed. Additionally, real-time battery information, including battery output voltage and SoC, was acquired and displayed by an automatic monitoring system. The designed system is valuable for all battery application cases.

A remaining charging electric quantity based pack available capacity optimization method considering aging inconsistency

Regarding battery packs in electric vehicles (EVs), the variations in the electric quantity and capacity of a pack due to aging inevitably affects its available capacity and performance. Thus, it is necessary to optimize the available capacity of the battery pack. Focusing on this problem, this paper proposes a novel concept of the remaining charging electric quantity (RCQ) to describe the battery state while considering aging. Then, a dual adaptive dual extended Kalman filter (Dual-ADEKF) algorithm is utilized to identify the model parameters and RCQ of each cell under working conditions. Furthermore, the actual RCQ of the battery pack after constant current charging is predicted while considering the aging caused capacity degradation and the increase in the DC internal resistance. To maximize the available capacity, a bidirectional active balancing system is used to adjust the electric quantity distribution in the pack to ensure that the RCQ of each cell is consistent; the available capacity of the pack is then determined by the battery with the lowest actual RCQ. The results show that the available capacity estimation of aged batteries while considering the influence of capacity degeneration and the DC internal resistance is effectively achieved. To test a battery pack with an initial capacity and an electric quantity inconsistency caused by aging, a federal urban driving schedule (FUDS) test is performed. The electric quantity to be balanced of each cell is calculated according to the RCQ estimation results with the target of maximizing the available capacity of the pack. It is verified that the available capacity of the pack increases by 11.7% compared to that before equalization, while the maximum electric quantity variation between cells is improved, decreasing from 20% to 2.6%. • A novel concept of remaining charging electric quantity(RCQ) to describe the battery aging state is proposed. • Battery internal resistance increase and capacity loss caused by aging are the major factors of RCQ degradation. • Model parameters and RCQ for aged batteries in pack are identified online using a Dual-ADEKF algorithm. • Actual RCQ consistency can ensure the maximum pack available capacity for aged battery pack.