With accurate battery modeling, circuit designers and automotive control algorithms developers can predict and optimize the battery performance. In this paper, an experimental verification of an accurate model for pri...With accurate battery modeling, circuit designers and automotive control algorithms developers can predict and optimize the battery performance. In this paper, an experimental verification of an accurate model for prismatic high current lithium-iron-phosphate battery is presented. An automotive TSLFP160AHA lithium-iron-phosphate battery bank is tested. The different capacity GBDLFMP60AH battery bank is used to validate the model extracted from the former battery. Effect of current, stacking and SOC upon the battery parameters performance is investigated. Six empirical equations are obtained to extract the prismatic type LiFePO4 model as a function of SOC. Based on comparing the measured and simulated data, a well accuracy of less than 50mV maximum error voltage with 1.7% operating time error referred to the measured data is achieved. The model can be easily modified to simulate different batteries and can be extended for wide ranges of different currents.展开更多
This study aims at developing an optimization framework for electric vehicle charging by considering different trade-offs between battery degradation and charging time.For the first time,the application of practical l...This study aims at developing an optimization framework for electric vehicle charging by considering different trade-offs between battery degradation and charging time.For the first time,the application of practical limitations on charging and cooling power is considered along with more detailed health models.Lithium iron phosphate battery is used as a case study to demonstrate the effectiveness of the proposed optimization framework.A coupled electro-thermal equivalent circuit model is used along with two battery health models to mathematically obtain optimal charging current profiles by considering stress factors of state-of-charge,charging rate,temperature and time.The optimization results demonstrate an improvement over the benchmark constant current–constant voltage(CCCV)charging protocol when considering both the charging time and battery health.A main difference between the optimal and the CCCV charging protocols is found to be an additional ability to apply constraints and adapt to initial conditions in the proposed optimal charging protocol.In a case study,for example,the‘optimal time’charging is found to take 12 min while the‘optimal health’charging profile suggests around 100 min for charging the battery from 25 to 75%state-of-charge.Any other trade-off between those two extreme cases is achievable using the proposed charging protocol as well.展开更多
文摘With accurate battery modeling, circuit designers and automotive control algorithms developers can predict and optimize the battery performance. In this paper, an experimental verification of an accurate model for prismatic high current lithium-iron-phosphate battery is presented. An automotive TSLFP160AHA lithium-iron-phosphate battery bank is tested. The different capacity GBDLFMP60AH battery bank is used to validate the model extracted from the former battery. Effect of current, stacking and SOC upon the battery parameters performance is investigated. Six empirical equations are obtained to extract the prismatic type LiFePO4 model as a function of SOC. Based on comparing the measured and simulated data, a well accuracy of less than 50mV maximum error voltage with 1.7% operating time error referred to the measured data is achieved. The model can be easily modified to simulate different batteries and can be extended for wide ranges of different currents.
文摘This study aims at developing an optimization framework for electric vehicle charging by considering different trade-offs between battery degradation and charging time.For the first time,the application of practical limitations on charging and cooling power is considered along with more detailed health models.Lithium iron phosphate battery is used as a case study to demonstrate the effectiveness of the proposed optimization framework.A coupled electro-thermal equivalent circuit model is used along with two battery health models to mathematically obtain optimal charging current profiles by considering stress factors of state-of-charge,charging rate,temperature and time.The optimization results demonstrate an improvement over the benchmark constant current–constant voltage(CCCV)charging protocol when considering both the charging time and battery health.A main difference between the optimal and the CCCV charging protocols is found to be an additional ability to apply constraints and adapt to initial conditions in the proposed optimal charging protocol.In a case study,for example,the‘optimal time’charging is found to take 12 min while the‘optimal health’charging profile suggests around 100 min for charging the battery from 25 to 75%state-of-charge.Any other trade-off between those two extreme cases is achievable using the proposed charging protocol as well.
