A Novel Real-Time State-of-Health and State-of-Charge Co-Estimation Method for LiFePO_4 Battery

The state of charge （SOC） and state of health （SOH） are two of the most important parameters of Li-ion batteries in industrial production and in practical applications. The real-time estimation for these two parameters is crucial to realize a safe and reliable battery application. However, this is a great problem for LiFePO4 batteries due to the large constant potential plateau in the charge/discharge process. Here we propose a combined SOC and SOH co-estimation method based on the experimental test under the simulating electric vehicle working condition. A first-order resistance-capacitance equivalent circuit is used to model the battery cell, and three parameter values, ohmic resistance （Rs）, parallel resistance （Rp） and parallel capacity （Cp）, are identified from a real-time experimental test. Finally we find that Rp and Cp could be utilized to make a judgement on the SOIl. More importantly, the linear relationship between Cp and the SOC is established to make the estimation of the SOC for the first time.

Potentiometric Measurement of State-of-Charge of Lead-Acid Batteries Using Polymeric Ferrocene and Quinones Derivatives

Measurement of state-of-charge of lead-acid batteries using potentiometric sensors would be convenient;however, most of the electrochemical couples are either soluble or are unstable in the battery electrolyte. This paper describes the results of an investigation of poly (divinylferrocene) (PDVF) and Poly(diethynylanthraquinone) (PAQ) couples in sulfuric acid with the view to developing a potentiometric sensor for lead-acid batteries. These compounds were both found to be quite stable and undergo reversible reduction/oxidation in sulfuric acid media. Their redox potential difference varied linearly with sulfuric acid concentration in the range of 1 M - 5 M (i.e. simulated lead-acid electrolyte during battery charge/discharge cycles). A sensor based on these compounds has been investigated.

Kalman Filters versus Neural Networks in Battery State-of-Charge Estimation: A Comparative Study

Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended Kalman filters (EKF) and artificial neural networks (ANN). EKF is a nonlinear optimal estimator that is used to estimate the inner state of a nonlinear dynamic system using a state-space model. On the other hand, ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. A pulse-discharge test was performed on a commercial lithium-ion (Li-ion) battery cell in order to collect data to evaluate those methods. Results are presented and compared.

Multi-Scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-Charge in Battery Energy Storage Systems

Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric vehicles.To overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical BESSs.Pearson correlation analysis is first employed to identify SOC-related parameters.These parameters are then input into a multi-layer GRU for point-wise feature extraction.Concurrently,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time intervals.Ultimately,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are rendered.Following extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.

Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neural networks for plug-in hybrid electric vehicles fuel economy

The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge （SOC） trajectory in plug-in hybrid electric vehicles （PHEVs）. The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categoriz- ing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm （AIA）. The objective function of the optimizer is derived from a computationally efficient artificial neural network （ANN） which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomic software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.

Optimal SOC Headroom of Pump Storage Hydropower for Maximizing Joint Revenue from Day-ahead and Real-time Markets Under Regional Transmission Organization Dispatch

In response to the increasing penetration of volatile and uncertain renewable energy,the regional transmission organizations(RTOs)have been recently focusing on enhancing the models of pump storage hydropower(PSH)plants,which are one of the key flexibility assets in the day-ahead(DA)and real-time(RT)markets,to further boost their flexibility provision potentials.Inspired by the recent research works that explored the potential benefits of excluding PSHs’cost-related terms from the objective functions of the DA market clearing model,this paper completes a rolling RT market scheme that is compatible with the DA market.Then,with the vision that PSHs could be permitted to submit state-of-charge(SOC)headrooms in the DA market and to release them in the RT market,this paper uncovers that PSHs could increase the total revenues from the two markets by optimizing their SOC headrooms,assisted by the proposed tri-level optimal SOC headroom model.Specifically,in the proposed tri-level model,the middle and lower levels respectively mimic the DA and RT scheduling processes of PSHs,and the upper level determines the optimal headrooms to be submitted to the RTO for maximizing the total revenue from the two markets.Numerical case studies quantify the profitability of the optimal SOC headroom submissions as well as the associated financial risks.

A comparative study of different online model parameters identification methods for lithium-ion battery

Precise states estimation for the lithium-ion battery is one of the fundamental tasks in the battery management system(BMS),where building an accurate battery model is the first step in model-based estimation algorithms.To date,although the comparative studies on different battery models have been performed intensively,little attention is paid to the comparison among different online parameters identification methods regarding model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost.In this paper,based on the Thevenin model,the three most widely used online parameters identification methods,including extended Kalman filter(EKF),particle swarm optimization(PSO),and recursive least square(RLS),are evaluated comprehensively under static and dynamic tests.It is worth noting that,although the built model’s terminal voltage may well follow a measured curve,these identified model parameters may significantly out of reasonable range,which means that the error between measured and predicted terminal voltage cannot be seen as a gist to determine which model is the most accurate.To evaluate model accuracy more rigorously,battery state-of-charge(SOC)is further estimated based on identified model parameters under static and dynamic tests.The SOC prediction results show that EKF and RLS algorithms are more suitable to be used for online model parameters identification under static and dynamic tests,respectively.Moreover,the random offset is added into originally measured data to verify the robustness ability of different methods,whose results indicate EKF and RLS have more satisfactory ability against imprecisely sampled data under static and dynamic tests,respectively.Considering model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost simultaneously,EKF is recommended to be adopted to establish battery model in real application among these three most widely used methods.

Robust state of charge and state of health estimation for batteries using a novel multi model approach

Estimation of state-of-charge and state-of-health for batteries is one of the most important feature for modern battery management system(BMS).Robust or adaptive methods are the most investigated because a more intelligent BMS could lead to sensible cost reduction of the entire battery system.We propose a new robust method,called ERMES(extendible range multi-model estimator),for determining an estimated state-of-charge(SoC),an estimated state-of-health(SoH)and a prediction of uncertainty of the estimates(state-of-uncertainty—SoU),thanks to which it is possible to monitor the validity of the estimates and adjust it,extending the robustness against a wider range of uncertainty,if necessary.Specifically,a finite number of models in state-space form are considered starting from a modified Thevenin battery model.Each model is characterized by a hypothesis of SoH value.An iterated extended Kalman filter(EKF)is then applied to each model in parallel,estimating for each one the SoC state variable.Residual errors are then considered to fuse both the estimated SoC and SoH from the bank of EKF,yielding the overall SoC and SoH estimates,respectively.In addition,a figure of uncertainty of such estimates is also provided.

A novel data-driven method for mining battery open-circuit voltage characterization

Lithium-ion batteries(LiB)are widely used in electric vehicles(EVs)and battery energy storage systems,and accurate state estimation relying on the relationship between battery Open-Circuit-Voltage(OCV)and State-of-Charge(SOC)is the basis for their safe and efficient applications.To avoid the time-consuming lab test needed for obtaining OCV-SOC curves,this study proposes a data-driven universal method by using operation data collected onboard about the variation of OCV with ampere-hour(Ah).To guarantee high reliability,a series of constraints have been implemented.To verify the effectiveness of this method,the constructed OCV-SOC curves are used to estimate battery SOC and State-of-Health(SOH),which are compared with data from both lab tests and EV manufacturers.Results show that a higher accuracy can be achieved in the estimation of both SOC and SOH,for which the maximum deviations are less than 3.0%and 2.9%respectively.