TY - JOUR
T1 - Simple parameterization method using differential evolution algorithm to estimate state of charge for Li-ion batteries and packs for electric vehicles
AU - Sandoval-Chileño, Marco A.
AU - Cortez, Ricardo
AU - Castañeda, Luis A.
AU - Lozada-Castillo, Norma
AU - Vazquez-Arenas, Jorge
AU - Luviano-Juárez, Alberto
N1 - Publisher Copyright:
© 2023 John Wiley & Sons Ltd.
PY - 2024/2
Y1 - 2024/2
N2 - An accurate method is proposed to measure the electrical parameters describing the experimental charge-discharge curves of Li-ion batteries and a pack through an equivalent electric dynamic model based on heuristic optimization algorithms (eg, differential evolution approach). The accuracy of these parameters is critical for on-board state of charge and Health (SOH) estimations of numerous applications and devices. The procedure is constructed, based on the simple Thévenin electrical model. Charge-discharge experimental curves collected for different chemistry and capacity batteries are described with the model, revealing better estimation results with respect to other algorithms such as impedance-based methodologies (decreasing the error until 64.19%) and particle swarm optimization based approaches (decreasing the error until 94.02%). The proposal was assessed with different chemistries, capacities and with a 28 battery pack (LiFeMnPO (Formula presented.), 100 Ah) used in an electric vehicle.
AB - An accurate method is proposed to measure the electrical parameters describing the experimental charge-discharge curves of Li-ion batteries and a pack through an equivalent electric dynamic model based on heuristic optimization algorithms (eg, differential evolution approach). The accuracy of these parameters is critical for on-board state of charge and Health (SOH) estimations of numerous applications and devices. The procedure is constructed, based on the simple Thévenin electrical model. Charge-discharge experimental curves collected for different chemistry and capacity batteries are described with the model, revealing better estimation results with respect to other algorithms such as impedance-based methodologies (decreasing the error until 64.19%) and particle swarm optimization based approaches (decreasing the error until 94.02%). The proposal was assessed with different chemistries, capacities and with a 28 battery pack (LiFeMnPO (Formula presented.), 100 Ah) used in an electric vehicle.
KW - differential evolution
KW - electric vehicles
KW - Li-ion battery packs
KW - parameter identification
KW - state of charge estimation
UR - http://www.scopus.com/inward/record.url?scp=85167353487&partnerID=8YFLogxK
U2 - 10.1002/est2.509
DO - 10.1002/est2.509
M3 - Artículo
AN - SCOPUS:85167353487
SN - 2578-4862
VL - 6
JO - Energy Storage
JF - Energy Storage
IS - 1
M1 - e509
ER -