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In the search for better efficiency, an auxiliary energy system (AES) for electric vehicles (EVs) was designed, implemented, and tested. The system, which is composed of an ultracapacitor bank and a buck-boost converter, was installed in an EV, which is powered by a lead-acid battery pack and a 54-kW brushless dc motor. Two control strategies where developed: one based on heuristics and the other based on an optimization model using neural networks. These strategies were translated to algorithms and implemented in a digital signal processor, and their performance was evaluated in urban driving. The results were incorporated to an economic evaluation of the system, which shows that the reduction in costs would only justify the inclusion of this type of system in a lead-acid battery-powered vehicle if the battery life is extended by 50% or more, which is unlikely. The same results were extrapolated to a case in which the lead-acid batteries are replaced by a fuel cell. In this case, the costs of different power support systems were evaluated, such as ultracapacitors and high-specific-power lithium-based batteries. The results showed a significant cost reduction when AES configurations are included in contrast to a system powered by fuel cells only. Also, the cost reduction was higher when using ultracapacitors for this purpose.