Adaptive Energy Management Strategies for Series Hybrid Electric Wheel Loaders

• Main Authors: Pahkasalo, Carolina, Sollander, André
• Format: Others
• Language: English
• Published: Linköpings universitet, Fordonssystem 2020
• Subjects: A-ECMS; ECMS; Equivalent Consumption Minimization Strategy; Dynamic Programming; Hybrid Electric Wheel Loader; Pattern Recognition; Machine Learning; Neural Networks; Learning Vector Quantization; Optimal Control; Control Engineering; Reglerteknik; Vehicle Engineering; Farkostteknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166284

An emerging technology is the hybridization of wheel loaders. Since wheel loaders commonly operate in repetitive cycles it should be possible to use this information to develop an efficient energy management strategy that decreases fuel consumption. The purpose of this thesis is to evaluate if and how this can be done in a real-time online application. The strategy that is developed is based on pattern recognition and Equivalent Consumption Minimization Strategy (ECMS), which together is called Adaptive ECMS (A-ECMS). Pattern recognition uses information about the repetitive cycles and predicts the operating cycle, which can be done with Neural Network or Rule-Based methods. The prediction is then used in ECMS to compute the optimal power distribution of fuel and battery power. For a robust system it is important with stability implementations in ECMS to protect the machine, which can be done by adjusting the cost function that is minimized. The result from these implementations in a quasistatic simulation environment is an improvement in fuel consumption by 7.59 % compared to not utilizing the battery at all.