• Energy Research
• 2016-2025close

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.

State-of-health (SOH) estimation is necessary for lithium ion batteries due to ineluctable battery ageing. Existing SOH estimation methods mainly focus on voltage characteristics without considering temperature variation in the process of health degradation. In this article, we propose a novel SOH estimation method based on battery surface temperature. The differential temperature curves during constant charging are analyzed and found to be strongly related to SOH. Part of the differential temperature curves in a voltage range is adopted to establish a relationship with SOH using support vector regression. The influence of battery discrepancy, voltage range, and sampling step are systematically discussed and the best combination of voltage range and sampling step is determined using leave-one-out validation. The proposed method is then validated and compared with an incremental capacity analysis (ICA)-based SOH estimation method using the Oxford and NASA datasets, which were collected from different cells under different conditions, respectively. The results show that the proposed method is capable of estimating SOH with the root-mean-square error less than 3.62% and 2.49%, respectively. In addition, the proposed method can improve the overall SOH estimation accuracy and robustness by combining with the ICA-based method with little computational burden.

Abstract An echelon internal heating method based on alternating current has exhibited many advantages of high heat generation rate and thermal uniformity for fast battery heating at low temperatures. However, inappropriate parameter settings for the echelon internal heating method are prone to risk battery health due to side reactions. To address this problem, a three-electrode cell is fabricated to investigate the effect of the echelon internal heating method on cell health at low temperatures. A fractional order circuit model is used to determine the optimal current amplitude and frequency of alternating current at different temperatures for preheating the cell. After many cycles of preheating the cell, capacity calibration results and incremental capacity curves show no apparent detrimental effect of the proposed heating method on cell health.

In this paper, an enhanced multi-constraint (MC) state of power (SOP) estimation algorithm in a prediction window up to 120 s is developed for lithium-ion batteries in electric vehicles (EVs). First, a novel regression-based algorithm (RBA) incorporating a model parameter forward prediction is devised for voltage-constraint SOP estimation to reduce battery linearization error and improve model accuracy by estimating model parameters to the end of a prediction window. The convergence condition is analytically formulated and can be satisfied over a whole battery operating range. Second, an improved state of energy (SOE)-constraint SOP estimation algorithm is proposed from a more practical perspective by considering battery terminal voltage variation in a prediction window. Together with the other constraints, the enhanced MC SOP estimation algorithm is achieved, which specifies a dynamic safe operating area for power regulation in EVs. Moreover, an incremental pulse test is designed to obtain reference SOPs in high fidelity under static conditions and dynamic load profiles. Simulations and experimental results demonstrate the improved accuracy of the enhanced MC SOP estimation algorithm over the conventional MC online SOP estimation algorithm.

With rapid transportation electrification worldwide, lithium-ion batteries have gained much attention for energy storage in electric vehicles (EVs). State of power (SOP) is one of the key states of lithium-ion batteries for EVs to optimise power flow, thereby requiring accurate online estimation. Equivalent circuit model (ECM)-based methods are considered as the mainstream technique for online SOP estimation. They primarily vary in their basic principle, technical contribution, and validation approach, which have not been systematically reviewed. This paper provides an overview of the improvements on ECM-based online SOP estimation methods in the past decade. Firstly, online SOP estimation methods are briefed, in terms of different operation modes, and their main pros and cons are also analysed accordingly. Secondly, technical contributions are reviewed from three aspects: battery modelling, online parameters identification, and SOP estimation. Thirdly, SOP testing methods are discussed, according to their accuracy and efficiency. Finally, the challenges and outlooks are presented to inspire researchers in this field for further developments in the future.

Abstract 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.

An adaptive H infinity filter approach is proposed to estimate the multistates including state of charge (SOC) and state of energy (SOE) for a lithium-ion battery pack. In the proposed approach, the covariance matching technique is used to adaptively update the covariance of system and observation noises and the recursive least square method is used to identify the battery model parameters in real time. The hardware-in-the-loop (HIL) platform for battery charge/discharge is set up to evaluate the accuracy and robustness of the SOC and the SOE estimation and compare the proposed approach with the multistate estimators using an extended Kalman filter and an H infinity filter. The experimental results indicate that the adaptive H infinity filter-based estimator is able to estimate the battery states in real time with the highest accuracy among the three filters.

Abstract State of charge (SOC) estimation constitutes a critical task of battery management systems. Conventional SOC estimation methods designed for dynamic profiles have difficulties in estimating SOC for LiFePO4 batteries due to their flat open circuit voltage characteristics in the middle range of SOC. In this study, a deep neural network (DNN) based method is proposed to estimate SOC with only 10-min charging voltage and current data as the input. This method enables fast and accurate SOC estimation with an error of less than 2.03% over the entire battery SOC range. Thus, it can be used to calibrate the SOC estimation for the Ampere-hour counting method. We also demonstrate that by incorporating the DNN into a Kalman filter, the robustness of SOC estimation against random noises and error spikes can be improved. In the case of significant disturbances, the method still maintains a root mean square error of 0.385%. Moreover, the trained DNN can quickly adapt to various scenarios, including different ageing states and battery types charged at different rates, thanks to the transfer learning nature. Compared with developing a new DNN, transfer learning can provide more accurate estimation results at less training costs. By only fine-tuning one layer of the pre-trained DNN, the root mean square error can be less than 3.146% and 2.315% for aged batteries and different battery types, respectively. When more layers are fine-tuned, superior performance can be achieved.

Battery model is the basis of battery efficient and safe management. The widely used equivalent circuit model (ECM) generally shows poor behavior in predicting battery terminal voltage at low sate of charge (SOC), increasing the risk in the urgent use of a battery at low voltage greatly. To model strong nonlinearity of battery open circuit voltage (OCV), a solid-phase diffusion equation based surface SOC is proposed to characterize OCV behavior and establish the new structure of the enhanced ECM to describe low SOC behavior more precisely. Finally, a battery test platform was built to conduct battery tests for model validation. The results show that the root mean square error (RMSE) of the battery terminal voltage obtained from the proposed model at low SOC has been reduced to 8 mV compared with the RMSE of 17 mV from the traditional ECM model. It is expected that the proposed model can be employed in battery management systems to effectively improve the reliability and safety of emergency use of a battery at low SOC.