By Johannes Unger, Marcus Quasthoff, Stefan Jakubek
This publication analyzes the most difficulties within the real-time regulate of parallel hybrid electrical powertrains in non-road functions that paintings in non-stop excessive dynamic operation. It additionally presents useful insights into maximizing the power potency and drivability of such powertrains.
It introduces an energy-management keep watch over constitution, which considers the entire actual powertrain constraints and makes use of novel methodologies to foretell the long run load specifications to optimize the controller output by way of the full paintings cycle of a non-road automobile. the burden prediction incorporates a method for non permanent lots in addition to cycle detection technique for a complete load cycle. during this approach, the strength potency may be maximized, and gas intake and exhaust emissions at the same time reduced.
Readers achieve deep insights into the themes that must be thought of in designing an strength and battery administration process for non-road autos. It additionally turns into transparent that just a blend of administration structures can considerably raise the functionality of a controller.
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Additional info for Energy Efficient Non-Road Hybrid Electric Vehicles: Advanced Modeling and Control (SpringerBriefs in Applied Sciences and Technology)
2013b). The second term in Eq. 17) is obtained by the single derivatives of the parameter sensitivity vectors with respect to the model input, which are based on the derivative of the regressor ϕ(k, θ ) as defined in Eq. 1) with respect to the input U (r ). Denoting the derivative of ϕ(k, θ ) by d yˆ (k − n, θ) d yˆ (k − 1, θ) dϕ T (k, θ) = ... δ1l δ(k−1)r . . 19) where δi j is the Kronecker delta function and l corresponds to the current input of the LMN battery model. The former model output with respect to the input U (r ) is recursively calculated and follows to ∂ yˆ (k, θ ) d yˆ (k, θ ) = dU (r ) ∂ yˆ (k − 1, θ ) · d yˆ (k − 1, θ ) dU (r ) recursive calculation ∂ yˆ (k, θ ) d yˆ (k − n, θ ) ∂ yˆ (k, θ ) + , k > r.
Consequentially, two possibilities can be used for current constraints: 1. High dynamic excitation between physical minimum/maximum current. 2. High dynamic excitation between load ranges, frequently used in operation. The first approach simply defines the current constraints at the physical minimum and maximum values, while the second approach defines the constraints using a real load cycle analysis that is realized as follows: step 1. Select a representative load cycle. step 2. Determine the distribution density of the load by a histogram with a defined number of intervals.
Note that the SoC estimation approach is applicable for cell and module SoC estimation, respectively, and is therefore developed for the general case. 1 General Architecture of the SoC Observer Scheme The estimation of the SoC of a battery can be obtained by two practical principles. First, measuring the open-circuit voltage leads to an accurate estimate of the SoC if the battery has been in standby mode for a sufficiently long period of time, but cannot be used during operation. 32) 0 where SoCinit is the initial SoC, Q c,batt is the battery capacity, and ηbatt,cou is the charge efficiency.
Energy Efficient Non-Road Hybrid Electric Vehicles: Advanced Modeling and Control (SpringerBriefs in Applied Sciences and Technology) by Johannes Unger, Marcus Quasthoff, Stefan Jakubek