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The purpose of this paper is to propose and compare two energy management strategies (EMSs). First, a classic method based on frequency separation with fixed limits on fuel cell (FC) power is presented and tested. Then, the improvement of the classic strategy is developed and implemented when the main enhancements are its ease of implementation, hydrogen economy and extending hybrid source lifetime.

The proposed EMS is developed using an online variable power limitation of the FC depending on the battery state of charge while ensuring that the energy of batteries remains in its operating depth of discharge (DOD) range.

In the objective to show the benefits of the developed strategy, a comparative analysis was conducted between two strategies. The simulation and experimental results show the effectiveness and gains obtained by the improved energy management system (IEMS) in terms of fuel economy (13 per cent) and decreasing the applied stress on the FC (22 per cent) which leads to a longer life span of the whole system.

The proposed approach is developed and tested by simulation. To confirm it, a test bench has been assembled as hardware in the loop (HIL) real-time system. The presented experimental results confirm the efficiency and show the providing gains of the IEMS.

The great value of offline identification of machine parameters is when the machine manufacturer does not provide its parameters. Most machine control strategies require parameter values, and some circumstances in the industrial sector only require offline identification. This paper aims to present a new offline method for estimating induction motor parameters based on least squares and a salp swarm algorithm (SSA).

The central concept is to use the classic least squares (LS) method to acquire the majority of induction machine (IM) constant parameters, followed by the SSA method to obtain all parameters and minimize errors.

The obtained results showed that the LS method gives good results in simulation based on the assumption that the measurements are noise-free. However, unlike in simulations, the LS method is unable to accurately identify the machine’s parameters during the experimental test. On the contrary, the SSA method proves higher efficiency and more precision for IM parameter estimation in both simulations and experimental tests.

After performing a primary identification using the technique of least squares, the initial intention of this study was to apply the SSA for the purpose of identifying all of the machine’s parameters and minimizing errors. These two approaches use the same measurement from a simple running test of an IM, and they offer a quick processing time. Therefore, this combined offline strategy provides a reliable model based on the identified parameters.