Battery Management System for Future Electric Vehicles

• Other Authors: Söffker, Dirk (Editor), Moulik, Bedatri (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: History of engineering & technology; state of charge (SOC); joint estimation; lithium-ion battery; variational Bayesian approximation; dual extended Kalman filter (DEKF); measurement statistic uncertainty; electric vehicles; renewable energy sources; microgrid; economic dispatching; capacity allocation; cooperative optimization; SOC; second-order RC model; model parameter optimization; AUKF; small-signal modeling; battery energy storage system; battery management system; control; stability; dynamic response; wireless power; state-of-charge; electric vehicle; LiFePO4 batteries; state of charge (SoC); Butler-Volmer equation; Arrhenius; Peukert; coulomb efficiency; back propagation neural network (BPNN); torque and battery distribution; particle swarm optimization; air-cooled BTMS; compact lithium ion battery module; ANN; battery electric vehicles; battery management; hybrid energy storage; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimi...