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

Battery State-of-Charge Estimation with Extended Kalman-Filter using Third-Order Thevenin Model

Lithium-ion battery has become the mainstream energy storage element of the electric vehicle. One of the challenges in electric vehicle development is the state-of-charge estimation of battery. Accurate estimation of state-of-charge is vital to indicate the remaining capacity of the battery and it will eventually maximize the battery performance and ensures the safe operation of the battery. This paper studied on the application of extended Kalman-filter and third order Thevenin equivalent circuit model in state-of-charge estimation of lithium ferro phosphate battery. Random test and pulse discharge test are conducted to obtain the accurate battery model. The simulation and experimental results are compared to validate the proposed state-of-charge estimation method

Research of NiMH Battery Modeling and Simulation Based on Linear Regression Analysis Method

 The battery state-of-charge estimation was one of core issues in the development of electric vehicles battery management system, and higher accurate model was needed in state-of-charge estimation correctly. Therefore, accurate battery modeling and simulation was researched here. The Thevenin equivalent circuit model of NiMH battery was established for the poor accuracy of traditional model. Based on the data which were brought from the 6V 6Ah NiMH battery hybrid pulse cycling test experiments, Thevenin model parameters were identified by means of the linear regression analysis method. Then, the battery equivalent circuit simulating model was built in the MATLAB/Simulink environment. The simulation and experimental results showed that the model has better accuracy and can be used to guide the battery state-of-charge estimation

State of Charge Estimation for Batteries

In this thesis, a methodology to accurately estimate the state of charge (SOC ) of the batteries used in Hybrid Electric Vehicles (HEV) is proposed. A linear relationship exists between open circuit voltage (VOC ) and SOC . In the proposed scheme, a system theory approach is employed to identify the open circuit voltage of the battery, from which the state of charge of the battery is determined. This method is very unique because it estimates the VOC of the battery in the vehicle even under load conditions. A state variable approach yields a set of non-linear time varying equations that describe the dynamics of the battery. This non-linear time varying system is reduced to a linear time varying system by making certain reasonable assumptions. The observability Gramian is calculated for the new linearized system from which VOC is identified. The terminal voltage and the discharging current measurements are obtained from the battery with the help of ABC-150, a programmable power processing system. The SOC estimation of the battery under discharging conditions alone is considered in this thesis. The VOC is then used to estimate the state of charge of the battery by exploiting the linear relationship between them. The results of applying the proposed technique are found to be comparable to the actual experimental results

Electric vehicle battery model identification and state of charge estimation in real world driving cycles

This paper describes a study demonstrating a new method of state-of-charge (SoC) estimation for batteries in real-world electric vehicle applications. This method combines realtime model identification with an adaptive neuro-fuzzy inference system (ANFIS). In the study, investigations were carried down on a small-scale battery pack. An equivalent circuit network model of the pack was developed and validated using pulse-discharge experiments. The pack was then subjected to demands representing realistic WLTP and UDDS driving cycles obtained from a model of a representative electric vehicle, scaled match the size of the battery pack. A fast system identification technique was then used to estimate battery parameter values. One of these, open circuit voltage, was selected as suitable for SoC estimation, and this was used as the input to an ANFIS system which estimated the SoC. The results were verified by comparison to a theoretical Coulomb-counting method, and the new method was judged to be effective. The case study used a small 7.2 V NiMH battery pack, but the method described is applicable to packs of any size or chemistry

Observer Based Cylinder Charge Estimation for Spark-ignition Engines

Internal combustion engines require accurate cylinder charge estimation for determining engine torque, controlling air-to-fuel ratio (AFR), and ensuring high after-treatment efficiency. This is challenging due to the highly transient operating conditions that are common in automobile engines. The problem is further complicated by spark ignition (SI) engine technologies such as variable valve timing (VVT) and exhaust gas recirculation (EGR) which are applied to improve fuel economy and reduce pollutant emissions. With manifold filling/emptying/mixing phenomenon and different actuator response times, these technologies significantly increase the complexity of cylinder charge estimation. Current cylinder charge estimation methodologies require a combination of sensors and empirical models to deal with the high degrees of control freedom existent on the engine. But these methods have the drawbacks of great dependency on accurate calibration and poor transient performance. Most importantly, the current methods isolate feed-forward cylinder charge estimation and feedback AFR control. When there is discrepancy between target lambda value and sensed lambda value at exhaust side, the current control/estimation method will trim the fuel injection amount no matter where the error source is. As a matter of fact, the error might come from the throttle flow estimation, the fuel injection flow estimation, EGR flow estimation, or any combination of these error sources. Increased air-path complexity and drawbacks of traditional methods drive the need for cost effective solutions that produce high air/EGR/fuel charge estimation accuracy with the ability to identify the error source while minimizing sensor cost, computational effort, and calibration time. This research first evaluates the existing work on air charge estimation for SI engines with massive experimental tests covering various operating conditions, which are designed for the algorithm verification of this research. Then several estimation methods which utilize both Manifold Absolute Pressure (MAP) and Mass Air Flow (MAF) sensors are studied and analyzed. Reduction of calibration effort and improvement of accuracy are observed from the proposed cylinder air charge estimation methods. Following that, a model is built to study the engine gas path dynamics and characteristics and then simplified to provide system dynamic basis for the following estimation algorithm development. Using the developed model, a disturbance observer based cylinder charge estimation technique is developed based on a combination of sensors including MAF, MAP, and exhaust lambda sensors. This developed algorithm significantly improves engine states estimation accuracy compared to conventional Single-Input-Single-Output (SISO) methods. Also, the augmentation of disturbance observation is able to pin point the source of the estimation error. Through experimental validation, using the developed estimation method with proper parameters, the error source of estimation can be identified and rectified when disturbance is introduced to throttle flow model, EGR flow model, fuel injection flow model or any combination of these models. The structure of the proposed algorithm should adapt to most SI engine configurations. It can help the engine controller to mitigate modeling errors thus improve the performance of physics model based engine control especially AFR control

Open circuit voltage and state of charge relationship functional optimization for the working state monitoring of the aerial lithium-ion battery pack.

The aerial lithium-ion battery pack works differently from the usual battery packs, the working characteristic of which is intermittent supplement charge and instantaneous large current discharge. An adaptive state of charge estimation method combined with the output voltage tracking strategy is proposed by using the reduced particle - unscented Kalman filter, which is based on the reaction mechanism and experimental characteristic analysis. The improved splice equivalent circuit model is constructed together with its state-space description, in which the operating characteristics can be obtained. The relationship function between the open circuit voltage and the state of charge is analyzed and especially optimized. The feasibility and accuracy characteristics are tested by using the aerial lithium-ion battery pack experimental samples with seven series-connected battery cells. Experimental results show that the state of charge estimation error is less than 2.00%. The proposed method achieves the state of charge estimation accurately for the aerial lithium-ion battery pack, which provides a core avenue for its high-power supply security