Real-time state of charge estimation of electrochemical model for lithium-ion battery

This paper proposes the real-time Kalman filter based observer for Lithium-ion concentration estimation for the electrochemical battery model. Since the computation limitation of real-time battery management system (BMS) micro-processor, the battery model which is utilized in observer has been further simplified. In this paper, the Kalman filter based observer is applied on a reduced order model of single particle model to reduce computational burden for real-time applications. Both solid phase surface lithium concentration and battery state of charge (SoC) can be estimated with real-time capability. Software simulation results and the availability comparison of observers in different Hardware-in- the-loop simulation setups demonstrate the performance of the proposed method in state estimation and real-time application

Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.Comment: Submitted to the Journal of Power Source

Infrared imaging investigation of temperature fluctuation and spatial distribution for a large laminated lithium ion power battery

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The present study investigates the thermal behaviors of a naturally cooled NCM-type LIB (LiNi1−x−yCoxMnyO2 as cathode) from an experimental and systematic approach. The temperature distribution was acquired for different discharge rates and Depth of Discharge (DOD) by the infrared imaging (IR) technology. Two new factors, the temperature variance ( ) and local overheating index (LOH index), were proposed to assess the temperature fluctuation and distribution. Results showed that the heat generation rate was higher on the cathode side than that on the anode side due to the different resistivity of current collectors. For a low-power discharge, the eventual stable high-temperature zone occurred in the center of the battery, while with a high-power discharge, the upper part of the battery was the high temperature region from the very beginning of discharge. It was found that the temperature variance ( ) and local overheating index (LOH index) were capable of holistically exhibiting the temperature non-uniformity both on numerical fluctuation and spatial distribution with varying discharge rates and DOD. With increasing the discharge rate and DOD, temperature distribution showed an increasingly non-uniform trend, especially at the initial and final stage of high-power discharge, the heat accumulation and concentration area increased rapidly

Electrochemical-thermal Analysis of High Capacity Li-ion Pouch Cell for Automotive Applications

Latest regulatory trends implemented in order to limit emissions combined with research advances in alternative fuels have paved the road toward vehicle electrification. Major original equipment manufacturers (\acrshort{OEM}s) have already marketed electric vehicles in large scale but apart from business strategies and policies, the real engineering problems must be addressed. Lithium-ion batteries are a promising technology for energy storage; however, their low energy density and complex electro-chemical nature, compared to fossil fuels, presents additional challenges. Their complex nature and strong temperature dependence during operation must be studied with additional accuracy, capable to predict their behavior. In this research, a pseudo two dimensional (\acrshort{P2D}) electro-chemical model, coupled with a 3D thermal energy balance for a recent high capacity \acrshort{NMC} pouch cell for automotive applications is developed. The electrochemical model with its temperature dependent parameters is validated at different temperatures and various discharge C-rates to accurately replicate the battery cell operational conditions. The sources of heat are distinguished and characterized via advanced electrochemical-modelling approach, in various battery operations and different thermal boundary conditions. For example, it was determined that the temperature rise during discharge at high C-rates, under natural convection, could result in thermal runaway, if managed incorrectly. Ohmic heat generation of current collectors and cell tabs is investigated and included. Hence, the thermal analysis provides insights on the current and voltage profiles causing the minimum thermal stress on the cell and the location of heat generation spatially and temporally during the battery discharge. Different modelling approximation of the cell are studied starting from the cell fundamental unit. This provides effective design considerations for the battery thermal management system (\acrshort{BTMS}) to enhance performance, cycle life and safety of future electrified vehicle energy storage systems

Temperature-Dependent Thévenin Model of a Li-Ion Battery for Automotive Management and Control

This paper focuses on the analysis of Li-ion battery behavior at different temperatures through the Thévenin electrical circuit model. First, evaluations for both steady-state and dynamic battery applications are provided, then an overview of the different battery models to describe their dynamic behavior is analyzed. The focus is dedicated to the double polarization Thévenin-based equivalent circuit model since it represents an optimal trade-off between accuracy and computation effort, which justifies its implementation in a Battery Management System (BMS) for automotive real-time monitoring and control. The model is composed of a voltage source, one series resistor and two series RC blocks. The Hybrid Pulse Power Characterization test (HPPC) is performed inside a climatic chamber to extract the electrical parameters of the model and their dependency from both temperature and State Of Charge (SOC). The load-current effects on the battery performance are not considered for the simplicity and lightness of the presented model. The presented procedure has broader validity and is mostly independent of cell format and Li-ion chemistry, despite a specific cylindrical battery cell is chosen for the study. The results of the test are suitable for the future implementation of a proper algorithm for SOC and State Of Health SOH estimations. Moreover, they provide an effective electrical and thermal characterization of the cell to evaluate the heat generation rate inside the cell

Kalman-variant estimators for state of charge in lithium-sulfur batteries

Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of ‘standard’ lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and ‘Coulomb counting’ are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit–network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort

Supervisory Control Optimization for a Series Hybrid Electric Vehicle with Consideration of Battery Thermal Management and Aging

This dissertation integrates battery thermal management and aging into the supervisory control optimization for a heavy-duty series hybrid electric vehicle (HEV). The framework for multi-objective optimization relies on novel implementation of the Dynamic Programing algorithm, and predictive models of critical phenomena. Electrochemistry based battery aging model is integrated into the framework to assesses the battery aging rate by considering instantaneous lithium ion (Li+) surface concentration rather than average concentration. This creates a large state-action space. Therefore, the computational effort required to solve a Deterministic or Stochastic Dynamic Programming becomes prohibitively intense, and a neuro-dynamic programming approach is proposed to remove the ‘curse of dimensionality’ in classical dynamic programming. First, unified simulation framework is developed for in-depth studies of series HEV system. The integration of a refrigerant system model enables prediction of energy use for cooling the battery pack. Side reaction, electrolyte decomposition, is considered as the main aging mechanism of LiFePO4/Graphite battery, and an electrochemical model is integrated to predict side reaction rate and the resulting fading of capacity and power. An approximate analytical solution is used to solve the partial difference equations (PDEs) for Li+ diffusion. Comparing with finite difference method, it largely reduces the number of states with only a slight penalty on prediction accuracy. This improves computational efficiency, and enables inclusion of the electrochemistry based aging model in the power management optimization framework. Next, a stochastic dynamic programming (SDP) approach is applied to the optimization of supervisory control. Auxiliary cooling power is included in addition to vehicle propulsion. Two objectives, fuel economy and battery life, are optimized by weighted sum method. To reduce the computation load, a simplified battery aging model coupled with equivalent circuit model is used in SDP optimization; Li+ diffusion dynamics are disregarded, and surface concentration is represented by the average concentration. This reduces the system state number to four with two control inputs. A real-time implementable strategy is generated and embedded into the supervisory controller. The result shows that SDP strategy can improve fuel economy and battery life simultaneously, comparing with Thermostatic SOC strategy. Further, the tradeoff between fuel consumption and active Li+ loss is studied under different battery temperature. Finally, the accuracy of battery aging model for optimization is improved by adding Li+ diffusion dynamics. This increases the number of states and brings challenges to classical dynamic programming algorithms. Hence, a neuro-dynamic programming (NDP) approach is proposed for the problem with large state-action space. It combines the idea of functional approximation and temporal difference learning with dynamic programming; in that case the computation load increases linearly with the number of parameters in the approximate function, rather than exponentially with state space. The result shows that ability of NDP to solve the complex control optimization problem reliably and efficiently. The battery-aging conscientious strategy generated by NDP optimization framework further improves battery life by 3.8% without penalty on fuel economy, compared to SDP strategy. Improvements of battery life compared to the heuristic strategy are much larger, on the order of 65%. This leads to progressively larger fuel economy gains over time