Abstract This paper seeks to identify the refrigeration load of a hybrid electric truck in order to find the demand power required by the energy management system. To meet this objective, in addition to the power consumption of the refrigerator, the vehicle mass needs to be estimated. The Recursive Least Squares (RLS) method with forgetting factors is applied for this estimation. As an example of the application of this parameter identification, the estimated parameters are fed to the energy control strategy of a parallel hybrid truck. The control system calculates the demand power at each instant based on estimated parameters. Then, it decides how much power should be provided by available energy sources to minimize the total energy consumption. The simulation results show that the parameter identification can estimate the vehicle mass and refrigeration load very well which is led to have fairly accurate power demand prediction. As a result, the energy management system can work to improve the fuel economy of the refrigerator truck. Introduction Hybrid vehicles have two different sources of energy. In hybrid electric vehicles (HEVs), an internal combustion engine (ICE) and electric motor are combined to provide demand power. Due to the fact that HEVs are more fuel efficient than conventional vehicles, the interest in converting different kinds of vehicles to hybrid vehicles is increasing. The motivation of this study is to convert a refrigerator truck to a parallel hybrid electric vehicle. In parallel configuration, as shown in Fig. 1, the engine and electric motor are both connected to the mechanical coupler and can simultaneously transmit power to drive the wheels. The refrigerator compressor is connected to the coupler too and can be driven by the engine and/or electric motor. In this configuration, there is no need for a separate engine to drive the compressor because when the engine is off, the electrical energy stored in the battery is used by electric motor to turn the compressor. Figure 1. Structure of a parallel hybrid electric refrigerator truck The task of the power management system of hybrid vehicles is to divide demand power between engine and electric motor in order to minimize fuel consumption in a driving cycle. In the refrigerator truck, calculating demand power is not possible because the mass and power consumption of the refrigerator are unknown. Based on the weight of freight, the mass of a vehicle can be different from one driving cycle to another. Moreover, power consumption of a refrigerator can vary during a driving cycle with respect to the evaporator and condenser temperatures. As a result, an estimation of these parameters is required to calculate the demand power. A considerable amount of literature has been published on identification of vehicle mass and some other parameters of the vehicle. Most of these studies have focused on simultaneous mass and road grade estimation. To meet this objective, various approaches such as: Kalman filtering, and recursive least squares have been proposed in [ 1], and [2] respectively. Vahidi et al. in [2] estimated mass and road grade for heavy-duty vehicles by using the RLS algorithm with Refrigeration Load Identification of Hybrid Electric Trucks2014-01-1897 Published 04/01/2014 Soheil Mohagheghi fard, Amir Khajepour, and Ayyoub Rezaeian University of Waterloo Chris J. Mendes CrossChasm Technologies CITATION: Mohagheghi fard, S., Khajepour, A., Rezaeian, A., and Mendes, C., "Refrigeration Load Identification of Hybrid Electric Trucks," SAE Technical Paper 2014-01-1897, 2014, doi:10.4271/2014-01-1897. Copyright © 2014 SAE InternationalDownloaded from SAE International by University of Minnesota, Wednesday, August 01, 2018multiple forgetting factors. They showed that “if the chosen forgetting factors reflect relative rate of variation of the parameters, both parameters can be estimated with good accuracy.” In this study, it i