Forklift truck performance simulation and fuel consumption estimation

Purpose: Forklift trucks are generally operated with frequent accelerations and stops, reverse and operations of load handling. This way of operation increases the energy losses and consequently the need for reduction of fuel consumption from forklift customers. This study aims to build a model to replicate the performance of forklifts during real operations and estimate fuel consumption without building a real prototype. Design/methodology/approach: AVL Cruise has been used to simulate forklift powertrain and hydraulic circuit. The driving cycles used for this study were in accordance with the standard VDI 2198. Artificial neural networks (ANNs), trained by the results of AVL Cruise simulations, have been used to forecast the fuel consumption for a large set of possible driving cycles. Findings: The comparison between simulated and experimental data verified that AVL Cruise model was able to simulate the performance of real forklifts, but the results were only valid for the specified driving cycle. The ANNs, trained by the results of AVL Cruise for a certain number of driving cycles, have been found effective to forecast the fuel consumption of a larger number of driving cycles following the prescriptions of the standard VDI 2198. Originality/value: A new method based on ANN, trained by AVL Cruise simulation results, has been introduced to forecast the forklift fuel consumption, reducing the computational time and the cost of experimental tests.