Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies

In this study, a framework is proposed for battery model identification to be applied in electric vehicle energy storage systems. The main advantage of the proposed approach is having capability to handle different battery chemistries. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and Lithium-Sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parametrisation is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. The use of identified parameters for battery state-of-charge (SOC) estimation is then discussed. It is demonstrated that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery’s open circuit voltage (OCV) is adequate for SOC estimation. However, Li-S battery SOC estimation can be challenging due to the chemistry’s unique features and the SOC cannot be estimated from the OCV-SOC curve alone because of its flat gradient. An observability analysis demonstrates that Li-S battery SOC is not observable using the common state-space representations in the literature. Finally, the problem’s solution is discussed using the proposed framework

Procedure for Selecting a Transmission Mode Dependent on the State-of-Charge and State-of-Health of a Lithium-ion Battery in Wireless Sensor Networks with Energy Harvesting Devices

Diverse methods and considerations have been proposed to manage the available energy in an efficient manner in Wireless Sensor Networks. By incorporating Energy Harvesting Devices in these type of networks it is possible to reduce the dependency of the availability of the Energy Storage Devices, particularly the lithium-ion battery. Recently, the State-of-Charge and State-of-Health of the battery have been considered as inputs for the design of the Medium- Access-Control protocols for Wireless Sensor Networks. In this article, different guidelines are proposed for the design of Medium-Access-Control protocols used in Wireless Sensor Networks with Energy Harvesting Devices considering the State-of-Charge and State-of-Health as indicators for the estimation of the transmission time of the sensor node. The proposed guidelines consider different currents used during the transmission to estimate the State-of-Charge and Stateof- Health of the battery. The incorporation of these indicators aim to improve the decision-making process of the sensor node when transmitting.Diverse methods and considerations have been proposed to manage the available energy in an efficient manner in Wireless Sensor Networks. By incorporating Energy Harvesting Devices in these type of networks it is possible to reduce the dependency of the availability of the Energy Storage Devices, particularly the lithium-ion battery. Recently, the State-of-Charge and State-of-Health of the battery have been considered as inputs for the design of the Medium- Access-Control protocols for Wireless Sensor Networks. In this article, different guidelines are proposed for the design of Medium-Access-Control protocols used in Wireless Sensor Networks with Energy Harvesting Devices considering the State-of-Charge and State-of-Health as indicators for the estimation of the transmission time of the sensor node. The proposed guidelines consider different currents used during the transmission to estimate the State-of-Charge and Stateof- Health of the battery. The incorporation of these indicators aim to improve the decision-making process of the sensor node when transmitting

Comparison between RLS-GA and RLS-PSO For Li-ion battery SOC and SOH estimation: a simulation study

This paper proposes a new method of concurrent SOC and SOH estimation using a combination of recursive least square (RLS) algorithm and particle swarm optimization (PSO). The RLS algorithm is equipped with multiple fixed forgetting factors (MFFF) which are optimized by PSO. The performance of the hybrid RLS-PSO is compared with the similar RLS which is optimized by single objective genetic algorithms (SOGA) as well as multi-objectives genetic algorithm (MOGA). Open circuit voltage (OCV) is treated as a parameter to be estimated at the same timewith internal resistance. Urban Dynamometer Driving Schedule (UDDS) is used as the input data. Simulation results show that the hybrid RLS-PSO algorithm provides little better performance than the hybrid RLS-SOGA algorithm in terms of mean square error (MSE) and a number of iteration. On the other hand, MOGA provides Pareto front containing optimum solutions where a specific solution can be selected to have OCV MSE performance as good as PSO

SIMULASI OPTIMASI PENGUKURAN STATE OF CHARGE BATERAI DENGAN INTEGRAL OBSERVER

The accuracy of state of charge (SoC) measurement has been an important key in the design of Battery Management System (BMS). A SoC measurement can prevent the battery from both overcharged and undercharged condition. One of the conventional methods for SoC estimation is coloumb counting (CC). This method has a drawback regarding to the accumulation of error propagation. To improve the accuracy of CC method, the integral observer could be added. In our work charge-discharge data simulation generated by PSIM was used to test the Integral observer performance. This research used general lithium-ion battery. It was found that this approach could significantly correct the error from the numerical integral calculation and discrete data input. The error of CC method at 4000 sec was found to reach 25%; however the error propagation could be decreased up to less than <3% by Integral observer

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC

Low-cost modular battery monitoring system for small scale testing

Electric vehicles have become a notable topic in recent years, and they are powered by a rechargeable lithium polymer (LiPo) battery. This paper describes a battery monitoring system that uses LiPo as the test subject to monitor its stability in the event of further improvement and safety limitations. A number of battery monitoring systems have been developed, but only a few are small and comprehensive. The proposed battery monitoring system will monitor the LiPo battery's state of charge (SoC), state of health (SoH), charge, and discharge using a user-friendly and affordable method. First, the data of the LiPo battery's discharge curve of voltage is collected using a voltage sensor connected in parallel with a closed loop circuit consisting of a LiPo battery and a DC motor connected in series. Second, the SoC is calculated based on the average total time spent before the voltage drop. Several trials are conducted to examine the consistency in monitoring the charge of allocated LiPo batteries. As a result of this study, a low-cost prototype for battery monitoring was developed, which proved to be reliable and perform well with a small margin of error