TY - JOUR
T1 - Estimating battery lifetimes in Solar Home System design using a practical modelling methodology
AU - Narayan, Nishant
AU - Papakosta, Thekla
AU - Vega-Garita, Victor
AU - Qin, Zian
AU - Popovic-Gerber, Jelena
AU - Bauer, Pavol
AU - Zeman, Miroslav
PY - 2018
Y1 - 2018
N2 - The rapid increase in the adoption of Solar Home Systems (SHS) in recent times hopes to ameliorate the global problem of energy poverty. The battery is a vital but usually the most expensive part of an SHS; owing to the least lifetime among other SHS components, it is also the first to fail. Estimating battery lifetime is a critical task for SHS design. However, it is also a complex task due to the reliance on experimental data or modelling cell level electrochemical phenomena for specific battery technologies and application use-case. Another challenge is that the existing electrochemical models are not application-specific to Solar Home Systems. This paper presents a practical, non-empirical battery lifetime estimation methodology specific to the application and the available candidate battery choices. An application-specific SHS simulation is carried out, and the battery activity is analyzed. A practical dynamic battery lifetime estimation method is introduced, which captures the fading capacity of the battery dynamically through every micro-cycle. This method was compared with an overall non-empirical battery lifetime estimation method, and the dynamic lifetime estimation method was found to be more conservative but practical. Cyclic ageing of the battery was thus quantified and the relative lifetimes of 4 battery technologies are compared, viz. Lead-acid gel, Flooded lead-acid, Nickel-Cadmium (NiCd), and Lithium Iron Phosphate (LiFePO4) battery. For the same SHS use-case, State-of-Health (SOH) estimations from an empirical model for LiFePO4 is compared with those obtained from the described methodology, and the results are found to be within 2.8%. The relevance of this work in an SHS application is demonstrated through a delicate balance between battery sizing and lifetime. Based on the intended application and battery manufacturer's data, the practical methodology described in this paper can potentially help SHS designers in estimating battery lifetimes and therefore making optimal SHS design choices.
AB - The rapid increase in the adoption of Solar Home Systems (SHS) in recent times hopes to ameliorate the global problem of energy poverty. The battery is a vital but usually the most expensive part of an SHS; owing to the least lifetime among other SHS components, it is also the first to fail. Estimating battery lifetime is a critical task for SHS design. However, it is also a complex task due to the reliance on experimental data or modelling cell level electrochemical phenomena for specific battery technologies and application use-case. Another challenge is that the existing electrochemical models are not application-specific to Solar Home Systems. This paper presents a practical, non-empirical battery lifetime estimation methodology specific to the application and the available candidate battery choices. An application-specific SHS simulation is carried out, and the battery activity is analyzed. A practical dynamic battery lifetime estimation method is introduced, which captures the fading capacity of the battery dynamically through every micro-cycle. This method was compared with an overall non-empirical battery lifetime estimation method, and the dynamic lifetime estimation method was found to be more conservative but practical. Cyclic ageing of the battery was thus quantified and the relative lifetimes of 4 battery technologies are compared, viz. Lead-acid gel, Flooded lead-acid, Nickel-Cadmium (NiCd), and Lithium Iron Phosphate (LiFePO4) battery. For the same SHS use-case, State-of-Health (SOH) estimations from an empirical model for LiFePO4 is compared with those obtained from the described methodology, and the results are found to be within 2.8%. The relevance of this work in an SHS application is demonstrated through a delicate balance between battery sizing and lifetime. Based on the intended application and battery manufacturer's data, the practical methodology described in this paper can potentially help SHS designers in estimating battery lifetimes and therefore making optimal SHS design choices.
KW - Battery lifetime model
KW - Battery sizing
KW - Cyclic ageing
KW - Dynamic battery lifetime estimation
KW - Solar Home Systems
UR - http://www.scopus.com/inward/record.url?scp=85049870400&partnerID=8YFLogxK
UR - http://resolver.tudelft.nl/uuid:e1ff3160-1158-4bad-8e5d-23b740237c7f
U2 - 10.1016/j.apenergy.2018.06.152
DO - 10.1016/j.apenergy.2018.06.152
M3 - Article
AN - SCOPUS:85049870400
SN - 0306-2619
VL - 228
SP - 1629
EP - 1639
JO - Applied Energy
JF - Applied Energy
ER -
