Regenerative Intelligent Brake Control for Electric Motorcycles

energies

Article Regenerative Intelligent Brake Control for Electric Motorcycles

Juan Jesús Castillo Aguilar * ID , Javier Pérez Fernández, Juan María Velasco García and Juan Antonio Cabrera Carrillo ID

Department of Mechanical Engineering, University of Málaga, 29071 Málaga, Spain; [email protected] (J.P.F.); [email protected] (J.M.V.G.); [email protected] (J.A.C.C.) * Correspondence: [email protected]; Tel.: +34-951-952-372; Fax: +34-951-952-605

Received: 17 August 2017; Accepted: 13 October 2017; Published: 20 October 2017

Abstract: Vehicle models whose propulsion system is based on electric motors are increasing in number within the automobile industry. They will soon become a reliable alternative to vehicles with conventional propulsion systems. The main advantages of this type of vehicles are the non-emission of polluting gases and noise and the effectiveness of electric motors compared to combustion engines. Some of the disadvantages that electric vehicle manufacturers still have to solve are their low autonomy due to inefﬁcient energy storage systems, vehicle cost, which is still too high, and reducing the recharging time. Current regenerative systems in motorcycles are designed with a low ﬁxed maximum regeneration rate in order not to cause the rear wheel to slip when braking with the regenerative brake no matter what the road condition is. These types of systems do not make use of all the available regeneration power, since more importance is placed on safety when braking. An optimized regenerative braking strategy for two-wheeled vehicles is described is this work. This system is designed to recover the maximum energy in braking processes while maintaining the vehicle’s stability. In order to develop the previously described regenerative control, tyre forces, vehicle speed and road adhesion are obtained by means of an estimation algorithm. A based-on-fuzzy-logic algorithm is programmed to carry out an optimized control with this information. This system recuperates maximum braking power without compromising the rear wheel slip and safety. Simulations show that the system optimizes energy regeneration on every surface compared to a constant regeneration strategy.

Keywords: regeneration; electric vehicles; antilock brake system (ABS); fuzzy logic

1. Introduction A large number of countries are adopting policies to increase the number of electric vehicles in their ﬂeets. The aim of these policies is to improve air quality by reducing pollutant emissions, especially in urban areas [1]. In this environment, the relationship between braking time and the vehicle moving time is very high. This indicates that there is a large amount of energy wasted as heat. This wasted kinetic energy can be recovered in electric vehicles by means of a regenerative braking system [2–4]. In large cities, with frequent stops and intersections, the energy dissipated in braking can reach 34% of total traction energy [3]. In traditional vehicles, that energy is wasted as heat through the brake friction. For this reason, regeneration or energy recovery systems are being widely used in electric vehicles. Electric vehicles have the ability to easily implement traction and/or braking control systems on the drive wheels. Since the motor is already part of the system, it is only necessary to implement a minimum of software and hardware to control that torque in the wheel. The great advantage is that electric motors can also be used as power generators. When a positive torque is required, the motor

Energies 2017, 10, 1648; doi:10.3390/en10101648 www.mdpi.com/journal/energies Energies 2017, 10, 1648 2 of 16 consumes battery power and when a negative torque is applied, motor power is delivered to the batteries. Nowadays, traction and braking controls are widely implemented in vehicles, with the aim of reducing braking distance or providing better manoeuvrability in emergency situations. Some of these systems are the Antilock Brake System (ABS) or the Traction Control System (TCS). These systems have evolved from their origin, using increasingly sophisticated algorithms and complex control architectures. Fuzzy logic [5,6], sliding control [7–9], control by artificial neural networks [10,11] and nonlinear control [12,13] are examples of the most used control methods. These systems try to optimize the longitudinal and lateral force in the tire, obtaining the maximum available force in the wheel-road contact during braking and traction processes. At present, regeneration systems in commercial electric vehicles do not take advantage of the characteristics mentioned above. A maximum level of regeneration is fixed for safety or battery capacity reasons. Therefore, they do not take advantage of the maximum available longitudinal force in tire-road contact. This way, these systems do not get the highest possible regenerated energy. In addition, most of the research on regeneration systems has focused on four-wheeled vehicles, although some authors have addressed this issue for two-wheeled vehicles. Most of them only deal with the electrical part of the system and do not take into account vehicle-road interaction [14–17]. Robinson and Singh [18] developed a control technique that applies a braking torque to the rear wheel, regenerating that braking energy from the rear wheel to the battery system. Moreover, the system controls the slip to prevent the locking of the rear wheel during braking. Lin et al. [19] studied an ABS braking control for electric scooters based on regenerative braking. In this work the design of a regenerative braking system for electric motorcycles is carried out using control and estimation of the road adherence algorithms. The system recovers the maximum energy during the braking process with the rear wheel of the motorcycle, always guaranteeing the safety of the rider thanks to the incorporation of a wheel slip control system. As we have mentioned, both energy saving and safety are fundamental subjects of study in vehicles. The system proposed here aims to improve the safety of electric vehicles as well as save energy in a novel way. It makes use of a fuzzy control that estimates the road adhesion and determines the optimal regenerative braking torque without causing the wheel to slip. The proposed system will reduce the use of the traditional rear friction brake depending on road conditions thanks to the high braking torque that can be achieved with the electric motor. This article begins with the description of the dynamic model of the electric motor used for simulation in Section2. The method to estimate the speed of the vehicle as well as the detection of the type of road is included in Section3. The regenerative control system is described in Section4. The performance of the whole system is verified through simulations included in Section5. Finally, the conclusions are presented in Section6.

2. Electric Motor Model Modelling of the electric motor is required to know the torque on the rear wheel. The model is used in the simulations of the regeneration system. Our prototype motorcycle has a Permanent Magnet Alternate Current (PMAC) motor, which is powered by a controller. This controller is also powered from the battery system. The battery system is able to absorb the energy recovered during the regeneration. In our case, the maximum current regenerated by the system is limited to 150 A to prevent heating of the batteries. The maximum torque that can be applied is 80 Nm. The curve relating the torque to the angular speed of rotation of the motor is shown in Figure1. These curves have been obtained by means of a dynamometric test by varying the angular speed of the motor and the position of the accelerator. As shown in Figure1, a negative torque that brakes the vehicle is applied to the rear wheel of the motorcycle when the motor is in regeneration mode. Energies 2017, 10, 1648 3 of 16 Energies 2017, 10, 1648 3 of 16 Energies 2017, 10, 1648 3 of 16

Figure 1. Torque curves of the electric motor. FigureFigure 1.1. TorqueTorque curves of the electric electric motor. motor. As it can be seen, the curves are proportional to the Throttle or Regeneration Rate (TR). In this As it can be seen, the curves are proportional to the Throttle or Regeneration Rate (TR). In this paper,As it TR can = be[−1, seen, 1], where the curves −1 means are proportional 100% regeneration to the Throttleand 1 means or Regeneration 100% acceleration. Rate (TR). Previous In this paper, TR = [−1, 1], where −1 means 100% regeneration and 1 means 100% acceleration. Previous paper,curvesTR have= [− been1, 1], modelled where − using1 means a Look-up 100% regenerationtable. The rear and wheel 1 means torque 100%is obtained acceleration. by means Previous of the curves have been modelled using a Look-up table. The rear wheel torque is obtained by means of the curvesfollowing have expression: been modelled using a Look-up table. The rear wheel torque is obtained by means of the following expression: following expression: =∙(,) (1) =∙(,) (1) T=∙= ϕ· f((ωm,,TR) (1) (1) where T is the torque on the rear wheel, is the transmission ratio between the motor and the rear where T is the torque on the rear wheel, is the transmission ratio between the motor and the rear wherewheel,T is ω them is torquethe angular on the speed rear of wheel, the motor,ϕ is the TR transmission is the Throttle ratio or Regeneration between the rate motor and and f(∙) theis the rear wheel, ωm is the angular speed of the motor, TR is the Throttle or Regeneration rate and f(∙) is the wheel, ωm is the angular speed of the motor, TR is the Throttle or Regeneration rate and f(∙) is the wheel,adjustmentωm is thefunction angular of the speed look-up of the table. motor, TR is the Throttle or Regeneration rate and f (·) is the adjustment function of the look-up table. adjustmentThe controller function ofinstalled the look-up on the table. electric motorcycle has been modelled using a step input (Figure The controller installed on the electric motorcycle has been modelled using a step input (Figure 2).The The controllerresponse is installed approximated on the electricto a second motorcycle order system has been according modelled to the using expression a step input shown (Figure in the2 ). 2). The response is approximated to a second order system according to the expression shown in the Thefigure. response is approximated to a second order system according to the expression shown in the ﬁgure. figure.

1 1 2 ++1 0.007ss2 ++ 0.134 1 0.007ss2 ++ 0.134 1

Figure 2. Second order response model of the electric motor. FigureFigure 2. 2.Second Second orderorder response model of of the the electric electric motor. motor. The actual responses of the motor have been compared to the programmed model. Two step TheThe actual actual responses responses of of the the motormotor havehave beenbeen compared to to the the programmed programmed model. model. Two Two step step inputs have been used. The results are shown in Figure 3. As it can be seen, the model response inputs have been used. The results are shown in Figure 3. As it can be seen, the model response inputsmatches have with been measured used. The one resultsappropriately. are shown in Figure3. As it can be seen, the model response matchesmatches with with measured measured one one appropriately. appropriately.

Figure 3. Real and simulated response to step input. Figure 3. Real and simulated response to step input. Figure 3. Real and simulated response to step input.

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3. Estimation of Road Type and Vehicle Parameters EnergiesThe 2017 slip, 10, on1648 each of the wheels is used to determine the road type. The slip is deﬁned with4 of the 16 following equation in the case of braking: 3. Estimation of Road Type and Vehicle Parameters ω · r The slip on each of the wheels is used sto= determine1 − the road type. The slip is defined with the(2) vx following equation in the case of braking: where ω is the angular velocity of the wheel, r the radius∙ of the tire and vx the longitudinal velocity of =1− (2) the vehicle. An Extended Kalman Filter (EKF) [5] based on a model that simulates the straight line behavior where ω is the angular velocity of the wheel, r the radius of the tire and vx the longitudinal velocity of the motorcycle is used in order to determine the speed of the vehicle. This model is described next. of the vehicle. In Figure4 the basic geometry of a motorcycle is represented schematically. The equations that An Extended Kalman Filter (EKF) [5] based on a model that simulates the straight line behavior govern the dynamics of the vehicle are: of the motorcycle is used in order to determine the speed of the vehicle. This model is described next. In Figure 4 the basic geometry of a motorcycle.. . . is represented. 2 schematically. The equations that Max = M(x + θz) = Fxr − Cx (3) govern the dynamics of the vehicle are: .. . . Maz = =M(z − +θz + =g) =N−f + N r (3)(4) .. Iy=θ = N −rlr − +=Nf l f − Fxz+ (4)(5) =T =ϕ−·Km·I − (5)(6)

The equations deﬁning the front and rear=∙ suspensions ∙ can be simpliﬁed as: (6)

The equations defining the front and rear suspensions can. be simplified. as: Nr = −kr·(z − L0 + lr·θ) − cr·(z + lr·θ) (7) =− · (− + ·) − · + · (7) . . N = −k · z − L + l ·θ − c ·(z + l ·θ) (8) f=− ·−f 0+ f·−f· +f · (8) The parameters and variables used in this model are shownshown inin TableTable1 1..

θ

Figure 4. Simplified motorcycle model. Figure 4. Simpliﬁed motorcycle model.

Table 1. Motorcycle model parameters.

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Table 1. Motorcycle model parameters.

Description Description M Mass J Moment of inertia of the wheel ax Longitudinal acceleration Iy Inertia on the Y axis az Vertical Acceleration Nf Normal front force x Longitudinal displacement Nr Normal rear force z Vertical displacement lf Front half length θ Pitch angle lr Rear half-length Energies 2017, 10, 1648Rr Rear tire radius kf Stiffness of front suspension 5 of 16 T Torque applied on rear wheel kr Stiffness of rear suspension C Aerodynamic coefﬁcient c Front Damping C Aerodynamic coefficient cf f Front Damping Fxr Longitudinal rear wheel force cr Rear damping Fxr Longitudinal rear wheel force cr Rear damping ωr Rear wheel angular speed L0 Suspension length ωr Rear wheel angular speed L0 Suspension length Km Torque-Intensity Ratio I Intensity Km Torque-Intensity Ratio I Intensity

The controlcontrol modelmodel toto bebe usedused inin thisthis workwork isis shownshown inin FigureFigure5 5..

θ θ

Figure 5. Motorcycle regenerative controlcontrol scheme.scheme.

As it can be seen, the regeneration control block (4) has the optimum slip (sopt) as input. As it can be seen, the regeneration control block (4) has the optimum slip (sopt) as input. Optimum Optimum slip is defined as the slip value that produces the maximum longitudinal force during slip is defined as the slip value that produces the maximum longitudinal force during braking or braking or traction. This is the desired slip for the motorcycle to have the highest regeneration rate. traction. This is the desired slip for the motorcycle to have the highest regeneration rate. In addition, In addition, control block 4 also has the current slip (s) on the rear wheel of the motorcycle as input. control block 4 also has the current slip (s) on the rear wheel of the motorcycle as input. The output The output of this block is the throttle or regeneration rate. In our case, this rate is an input to the of this block is the throttle or regeneration rate. In our case, this rate is an input to the motor of motor of the motorcycle. the motorcycle. A parameter estimation algorithm is needed to obtain the inputs to the regeneration control, A parameter estimation algorithm is needed to obtain the inputs to the regeneration control, since these cannot be measured with sensors in the vehicle. This is carried out in blocks (2) and (3) of since these cannot be measured with sensors in the vehicle. This is carried out in blocks (2) and (3) of Figure 5. Figure5. The linear speed of the motorcycle, the longitudinal force and the coefficient of friction in the The linear speed of the motorcycle, the longitudinal force and the coefficient of friction in the rear rear wheel are estimated first. To this end, an Extended Kalman filter as proposed in [5] is used. wheel are estimated first. To this end, an Extended Kalman filter as proposed in [5] is used. The EKF is defined according to the following equations: The EKF is defined according to the following equations:

= ()+ (9) xk = fk−1(xk−1) + wk−1 (9) =ℎ()+ (10) where wk and vk represent the noise of the model and the measures respectively, xk is the state vector and jk is the measurement vector. The state vector is: () =,,,,, (11) and the measurement vector is: () = ,,, (12) A first order random walk is used to estimate the longitudinal and vertical forces. Therefore, the model equations are included next:

() =(−1) (13)

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jk = hk(xk) + vk (10) where wk and vk represent the noise of the model and the measures respectively, xk is the state vector and jk is the measurement vector. The state vector is:

h iT x(k) = Fx,r, Nr, Nf , vx, T (11) and the measurement vector is: . T j(k) = [ax, az, θ, I] (12) A ﬁrst order random walk is used to estimate the longitudinal and vertical forces. Therefore, the model equations are included next:

Fxr(k) = Fxr(k − 1) (13)

Nr(k) = Nr(k − 1) (14)

Nf (k) = Nf (k − 1) (15) F (k − 1) − C·v 2(k − 1) v (k) = v (k − 1) + ∆t· xr x − θ·v (16) x x M z T(k) = T(k − 1) (17)

Finally, the measurement equations are the following:

F (k) − C·v 2(k) a (k) = xr x (18) x M

Nf (k) + Nr(k) − M·g a (k) = (19) z M

.. Nr(k)·lr − Nf (k)·l f − Fxr(k)·z(k) θ(k) = (20) Iy T(k) I(k) = (21) Km The EKF algorithm estimates the longitudinal and vertical forces and the longitudinal speed of the motorcycle in addition to the rear wheel torque with the equations developed above. An estimate of the slip and the coefficient of friction can be made according to the following equations: ω (k)·R sest(k) = 1 − r r (22) vx(k) F (k) µest(k) = xr (23) Nr(k) Figure6 shows, by way of example, a simulation performed with BikeSim ® (Ann Arbor, MI, USA) where the parameters necessary for the determination of the slip and the coefficient of adhesion are estimated. The test consists of the following sections: 40 m of high adhesion surface, 40 m of low adhesion surface and a third section with high adhesion again. The initial speed is 120 km/h. In these simulations the signal from the sensors has been altered with zero-mean random noise. A subscript “k” in the legend indicates that it is an estimated magnitude. The parameters estimated by means of the EKF fit quite accurately to the values provided by the simulation software. It can be verified that the speed (vx), front vertical force (Fzf), rear vertical force (Fzr), rear traction force (Ft) and brake torque (T) are estimated correctly. Energies 2017, 10, 1648 6 of 16

() =(−1) (14)

() =(−1) (15) (−1) −· (−1) () = (−1) +∆· −· (16)

() =(−1) (17) Finally, the measurement equations are the following: () −· () () = (18)

() +() −· () = (19)

() · −() · −() ·() () = (20) () () = (21) The EKF algorithm estimates the longitudinal and vertical forces and the longitudinal speed of the motorcycle in addition to the rear wheel torque with the equations developed above. An estimate of the slip and the coefficient of friction can be made according to the following equations:

() ∙ () =1− (22) ()

() () = (23) () Figure 6 shows, by way of example, a simulation performed with BikeSim® (Ann Arbor, MI, USA) where the parameters necessary for the determination of the slip and the coefficient of adhesion are estimated. The test consists of the following sections: 40 m of high adhesion surface, 40 m of low adhesion surface and a third section with high adhesion again. The initial speed is 120 km/h. In these simulations the signal from the sensors has been altered with zero-mean random noise. A subscript “k” in the legend indicates that it is an estimated magnitude. The parameters estimated by means of the EKF fit quite accurately to the values provided by the simulation software. It can be verified that Energiesthe speed2017, 10 (v,x 1648), front vertical force (Fzf), rear vertical force (Fzr), rear traction force (Ft) and brake torque7 of 16 (T) are estimated correctly.

FigureFigure 6. 6.Estimation Estimation of speed, tractor effort effort and and vertical vertical forces. forces. Energies 2017, 10, 1648 7 of 16 Energies 2017, 10, 1648 7 of 16 Finally,Finally, the the followingfollowing figure figure shows shows the the results results obta obtainedined in a real in atest real carried test carriedout with outthe vehicle with the vehicledescribedFinally, described in theAppendix infollowing Appendix A. figureThisA. estimation Thisshows estimation the resultsalgorithm algorithm obta verificationined in verification a real test test was carried test performed was out performed with with the vehiclea with20% a described in Appendix A. This estimation algorithm verification test was performed with a 20% 20%constant constant regeneration regeneration rate. rate. Figure Figure 7 includes7 includes the the actual actual speed speed (vx (),v xmeasured), measured by bya high a high frequency frequency GlobalGlobalconstant Positioning Positioning regeneration System-based System-based rate. Figure speed speed 7 includes sensors sensors the and and actual the the measured measuredspeed (v brakex), brake measured torque. torque. by It It cana can high be be seenfrequency seen that that the speedtheGlobal speed is perfectly Positioning is perfectly estimated. System-based estimated. The brake The speed brake torque sensors torque is alsoand is correctlyalsothe measuredcorrectly estimated estimated brake except torque. except in It the canin finalthe be finalseen part partthat of the test,ofthe wherethe speed test, a iswhere higher perfectly a errorhigher estimated. is error observed. is The observed. brake torque is also correctly estimated except in the final part of the test, where a higher error is observed.

FigureFigure 7.7. EstimationEstimation of speed and and brake brake torque. torque. Figure 7. Estimation of speed and brake torque. These values are used to determine the road type and to obtain the optimal slip, as shown in FigureTheseThese 8. A values valuescontrol are are based used used on toto fuzzy determinedetermine logic is the theprogramm road road type typeed andto and estimate to toobtain obtain the the road theoptimal type. optimal Theslip, slip,ouputas shown as of shownthis in inblockFigure is 88. an. A Aindex control control called based based Road on on fuzzyCondition fuzzy logic logic Index is programm is (RCI), programmed relateded to estimate to to the estimate adherence the road the oftype. road the Theroad. type. ouput Finally, The of ouput thisthe ofoptimalblock this block is anslip index is is anobtained called index Roadthrough called Condition Road a Neural Condition Index Network. (RCI), Index related (RCI), to the related adherence to the of adherence the road. Finally, of the the road. Finally,optimal the slip optimal is obtained slip is through obtained a Neural through Network. a Neural Network.

Figure 8. Control of road type and estimation of optimum slip. FigureFigure 8. 8.Control Controlof of roadroad typetype and estimation of of optimum optimum slip. slip. The road type estimation block has the slip (s), the longitudinal coefficient of friction (μ) and the variationThe roadof the type longitudinal estimation coefficient block has with the sliprespect (s), theto the longitudinal slip (dμ/ds )coefficient as input. It of produces friction ( μan) andoutput the valuevariation indicating of the longitudinal the type of roadcoefficient on which with the respect vehi cleto the circulates. slip (dμ /Thisds) as value input. is It within produces the anrange output (0– 1.2),value where indicating 1.2 represents the type aof road road with on which the highest the vehi coefficientcle circulates. of adhesion This value and 0is represents within the the range lowest (0– one.1.2), where 1.2 represents a road with the highest coefficient of adhesion and 0 represents the lowest one. The input and output variables have been fuzzyfied by means of the membership functions indicatedThe inputin Figure and 9. output In addition, variables the ruleshave defibeenned fuzzy for thefied fuzzy by means control of are the presented membership in Table functions 2. indicated in Figure 9. In addition, the rules defined for the fuzzy control are presented in Table 2.

Energies 2017, 10, 1648 8 of 16

The road type estimation block has the slip (s), the longitudinal coefficient of friction (µ) and the variation of the longitudinal coefficient with respect to the slip (dµ/ds) as input. It produces an output value indicating the type of road on which the vehicle circulates. This value is within the range (0–1.2), where 1.2 represents a road with the highest coefficient of adhesion and 0 represents the lowest one. The input and output variables have been fuzzyfied by means of the membership functions Energies 2017, 10, 1648 8 of 16 indicated in Figure9. In addition, the rules defined for the fuzzy control are presented in Table2.

Figure 9. Membership functions for input and output variablesvariables of the road typetype detectiondetection block.block.

Finally, asas shown shown in in Figure Figure8, once 8, once the road the typeroad hastype been has obtained been obtained by the RCI by index,the RCI the index, optimum the slipoptimum is calculated. slip is calculated. In this work, In this the work, optimum the opti slipmum has beenslip has estimated been estimate by meansd by ofmeans a trained of a trained neural networkneural network using the using Burkhardt the Burkhardt model [model20]. The [20]. network The network has the has RCI the index, RCI index, obtained obtained from the from fuzzy the block,fuzzy block, and the and wheel the wheel slip angle slip asangle inputs. as inputs. The net Th providese net provides the optimum the optimum slip for slip each for surfaceeach surface from thesefrom these inputs. inputs. In our In case, our thecase, slip the angle slip angle has been has takenbeen taken as null. as null.

Table 2.2. RoadRoad controlcontrol rules.rules. Rule Number Friction Coefficient Slip dμ/ds RCI µ Rule Number1 Friction CoefﬁcientMPR Slipmid --- d /dsVSRF RCI 12 MPRMPR midhigh --- —ZRF VSRF 23 MPRMPR highzero Lmud — MRF ZRF 34 MPRMPR zerozero Mmud Lmud LRF MRF 45 MPRMPR zerozero Hmud Mmud LRF LRF 5 MPR zero Hmud LRF 6 PR mid --- SRF 6 PR mid — SRF 7 PR high --- VSRF 7 PR high — VSRF 88 PRPR zerozero Lmud Lmud LRF LRF 99 PRPR zerozero Mmud Mmud LRF LRF 1010 PRPR zerozero Hmud Hmud VLRF VLRF 1111 RMRM midmid --- —MRF MRF 1212 RMRM highhigh --- —MRF MRF 1313 RMRM zerozero Lmud Lmud LRF LRF 1414 RMRM zerozero Mmud Mmud VLRF VLRF 1515 RMRM zerozero Hmud Hmud HRF HRF 16 RN mid --- VLRF 17 RN high --- LRF 18 RN zero Lmud VLRF 19 RN zero Mmud HRF 20 RN zero Hmud ERF 21 MUR mid --- HRF 22 MUR high --- ERF 23 MUR zero Lmud HRF 24 MUR zero Mmud ERF

Energies 2017, 10, 1648 9 of 16

Table 2. Cont.

Rule Number Friction Coefﬁcient Slip dµ/ds RCI 16 RN mid — VLRF 17 RN high — LRF 18 RN zero Lmud VLRF 19 RN zero Mmud HRF 20 RN zero Hmud ERF 21 MUR mid — HRF 22 MUR high — ERF Energies 2017, 10, 164823 MUR zero Lmud HRF 9 of 16 24 MUR zero Mmud ERF 2525 MURMUR zerozero Hmud Hmud ERF ERF

4. Regenerative Control 4. Regenerative Control The last step in the control scheme is to obtain the level of regeneration that is desired in order Theto obtain last step the maximum in the control energy scheme recovered is to in obtain the brak theing level processes. of regeneration To this end, that a iscontrol desired based in order on to obtainfuzzy the maximumlogic has been energy developed recovered (Figure in the10). brakingThis fuzzy processes. block has To the this slip end, error a and control the variation based on of fuzzy logic hasthe error been in developed time as inputs, (Figure which 10 ).are This defined fuzzy as: block has the slip error and the variation of the error in time as inputs, which are deﬁned as: () =() −() (24) ∆() () −(−1) e(t) ==sopt(t) − s(t) (25) (24) ∆ ∆ As it can be seen, the slip error∆ ise (definedt) e (ast) −thee (differencet − 1) between the optimal slip and the = (25) actual slip at each instant of time. Once∆ tthe error and∆ tthe error variation have been calculated, the Aspercentage it can be of seen, regeneration the slip errorto be applied is deﬁned according as the differenceto the next block between is obtained. the optimal The output slip and variable the actual slip atof each the fuzzy instant block, of time. called Once Δ, is a the multiplying error and factor the error of the variation regeneration have rate been established calculated, in the the previous percentage time interval. Thus, the rate at time t is obtained from: of regeneration to be applied according to the next block is obtained. The output variable of the fuzzy block, called ∆, is a multiplying factor of the regeneration(+1) =∆∙ rate() established in the previous time(26) interval. Thus, theThe rate regeneration at time t is obtainedrate can never from: be less than a certain minimum value. This is so because if a null value were reached at an instant of time, the system would not be able to raise the rate again. On the other hand, the rate can never exceedTR a maximum(t + 1) = ∆value.·TR( Thist) maximum value is imposed by the (26) maximum bearable charge current of the battery system.

Figure 10. Membership functions for the input and output variables of the regeneration rate block. Figure 10. Membership functions for the input and output variables of the regeneration rate block. 5. Simulations

The vehicle dynamics simulation software BikeSim® was used for this purpose. Simulations were carried out on Simulink® (Natick, MA, USA), using BikeSim® as S-function. BikeSim® was incorporated to the model to simulate vehicle behaviour. The model has the outputs from the control system as inputs and provided the measures obtained in the vehicle. These simulations allow evaluating the potential and feasibility of the proposed regeneration algorithm. Table 3 shows the main characteristics of the vehicle used in the simulations.

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The regeneration rate can never be less than a certain minimum value. This is so because if a null value were reached at an instant of time, the system would not be able to raise the rate again. On the other hand, the rate can never exceed a maximum value. This maximum value is imposed by the maximum bearable charge current of the battery system.

5. Simulations The vehicle dynamics simulation software BikeSim® was used for this purpose. Simulations wereEnergies carried 2017, 10,out 1648on Simulink® (Natick, MA, USA), using BikeSim® as S-function. BikeSim®10 wasof 16 incorporated to the model to simulate vehicle behaviour. The model has the outputs from the Table 3. Motorcycle model parameters. control system as inputs and provided the measures obtained in the vehicle. These simulations allow evaluating the potential and feasibilityDescription of the proposed Value regeneration algorithm. Table3 shows the main characteristicsTotal of the mass vehicle used in the simulations. 275 kg Wheel radius 0.32 m Moment of Tableinertia 3. ofMotorcycle the wheel model parameters. 0.484 kg∙m2 Distance from the COG * to the front axle 0.86 m Distance from the COGDescription * to the rear Value axle 0.67 m HeightTotal mass of gravity centre 0.4 275 m kg FrontWheel area radius 0.6 0.32 m m2 AerodynamicMoment of inertia coefficient of the wheel 0.550.484 kg·m2 Motor-wheelDistance from transmission the COG * to ratio the front axle 1:6.4 0.86 m Distance from the* COGCOG* = to Centre the rear of axleGravity 0.67 m Height of gravity centre 0.4 m Front area 0.6 m2 5.1. Low Adhesion Condition Simulation Aerodynamic coefﬁcient 0.55 In the first place, aMotor-wheel transition between transmission a low ratio adhesion and a very 1:6.4 low adhesion surface has been simulated. The test begins at the low adhesion* COG = Centre surface. of Gravity. After 20 m the surface is changed to a very low adhesion surface. The initial speed is 60 km/h. The test ends when the motorcycle reaches 20 5.1.km/h. Low Maximum Adhesion Conditionregeneration Simulation has been limited to 50%. This rate of regeneration corresponds to an intensityIn the of ﬁrst 148 place, A, less a transitionthan 150 betweenA of the ama lowximum adhesion charge and current a very lowrecommended adhesion surface by the has battery been simulated.manufacturer. The test begins at the low adhesion surface. After 20 m the surface is changed to a very low adhesionIn Figure surface. 11a, The the initialoutput speed provided is 60 by km/h. the road The estimation test ends when algorithm the motorcycle and the neural reaches network 20 km/h. can Maximumbe observed. regeneration The system has is beenable limitedto detect to the 50%. change This rateof surface of regeneration and adapt corresponds the optimum to an slip intensity to the ofnew 148 conditions. A, less than 150 A of the maximum charge current recommended by the battery manufacturer. InThe Figure output 11 a,of thethe outputfuzzy block provided that byprovides the road the estimation regeneration algorithm rate is andshown the in neural Figure network 11b. It can be observed.verified how, The when system changing is able surface, to detect the the system change adapts of surface the rate and in adapt orderthe to obtain optimum the maximum slip to the newpossible conditions. regeneration on each surface.

(a) (b)

Figure 11. Estimation of road type and optimum slip (a) andand regenerationregeneration raterate ((bb).).

Finally, the slip and velocity evolution throughout the test is shown in Figure 12. The slip tends to reach the optimum slip, adapting its value after the change of surface.

Energies 2017, 10, 1648 11 of 16

The output of the fuzzy block that provides the regeneration rate is shown in Figure 11b. It can be veriﬁed how, when changing surface, the system adapts the rate in order to obtain the maximum possible regeneration on each surface. Finally, the slip and velocity evolution throughout the test is shown in Figure 12. The slip tends to Energiesreach the 2017 optimum, 10, 1648 slip, adapting its value after the change of surface. 11 of 16 Energies 2017, 10, 1648 11 of 16

Figure 12. Optimum slip, real slip and speed. FigureFigure 12.12. Optimum slip, realreal slipslip andand speed.speed. 5.2. High to Low Adhesion Transition Simulation 5.2.5.2. High to Low Adhesion TransitionTransition SimulationSimulation Secondly, a transition between a high adhesion surface and a low adhesion surface has been simulated.Secondly,Secondly, The aatest transitiontransition consists between betweenof the following aa highhigh adhesionadhesion sections: surface surface40 m of andand high aa lowlowadhesion adhesionadhesion surface, surfacesurface 40 m hashas of beenbeenlow adhesionsimulated.simulated. surface The test and consists a third ofsection the following with high sections: adhesion 40 again. m of The high initial adhesion speed surface, is 120 km/h. 40 m Figure of lowlow 13aadhesionadhesion shows surfacesurface the type and and of a surfacea third third section andsection the with withoptimal high high adhesion slip. adhesion As in again. the again. previous The The initial initial case, speed thespeed is estimation 120 is km/h.120 km/h. algorithms Figure Figure 13a quicklyshows13a shows the detect typethe surfacetype of surface of surfacechanges. and and the the optimal optimal slip. slip. As As in in the the previous previous case, case, the the estimation estimation algorithmsalgorithms quicklyquickly detectdetect surfacesurface changes.changes.

(a) (b) (a) (b) Figure 13. Estimation of road type and optimum slip (a) and regeneration rate (b). Figure 13.13. Estimation of road type andand optimumoptimum slipslip ((aa)) andand regenerationregeneration raterate ((bb).). Figure 13b shows the output of the fuzzy block which provides the regeneration rate. It is observedFigure that 13b the shows rate of the regeneration output of isthe maintain fuzzy blocked at thewhich maximum provides in the highregeneration adhesion rate. surface. It is Figure 13b shows the output of the fuzzy block which provides the regeneration rate. It is Thisobserved is due that to the the fact rate that, of regeneration even with that is maintainregeneraedtion at percentage, the maximum a slip in inthe the high rear adhesion axle close surface. to the observed that the rate of regeneration is maintained at the maximum in the high adhesion surface. optimumThis is due slip to isthe not fact achieved. that, even Once with the that low regenera adhesiontion surface percentage, has been a slip reached, in the the rear regeneration axle close to rate the This is due to the fact that, even with that regeneration percentage, a slip in the rear axle close to the decreasesoptimum inslip order is not to achieved. adjust the Once slip tothe the low new adhesion adhesion surface conditions has been (Figure reached, 14). the regeneration rate optimum slip is not achieved. Once the low adhesion surface has been reached, the regeneration rate decreases in order to adjust the slip to the new adhesion conditions (Figure 14). decreases in order to adjust the slip to the new adhesion conditions (Figure 14). Slip Slip Speed (km/h) Speed (km/h)

Figure 14. Optimum slip, real slip and speed. Figure 14. Optimum slip, real slip and speed.

Energies 2017, 10, 1648 11 of 16

Figure 12. Optimum slip, real slip and speed.

5.2. High to Low Adhesion Transition Simulation Secondly, a transition between a high adhesion surface and a low adhesion surface has been simulated. The test consists of the following sections: 40 m of high adhesion surface, 40 m of low adhesion surface and a third section with high adhesion again. The initial speed is 120 km/h. Figure 13a shows the type of surface and the optimal slip. As in the previous case, the estimation algorithms quickly detect surface changes.

(a) (b)

Figure 13. Estimation of road type and optimum slip (a) and regeneration rate (b).

Figure 13b shows the output of the fuzzy block which provides the regeneration rate. It is observed that the rate of regeneration is maintained at the maximum in the high adhesion surface. This is due to the fact that, even with that regeneration percentage, a slip in the rear axle close to the optimumEnergies 2017 slip, 10, 1648is not achieved. Once the low adhesion surface has been reached, the regeneration12 rate of 16 decreases in order to adjust the slip to the new adhesion conditions (Figure 14). Slip Speed (km/h)

Figure 14. Optimum slip, real slip and speed.

5.3. Controls Comparison Simulations have been carried out to verify that the new control manages to optimize regenerated energy. For this purpose, tests in which the vehicle starts at a speed of 80 km/h have been carried out. The test lasts ﬁve seconds. At the end of that time, the velocity achieved and the energy recovered are determined (Table4). The constant regeneration rate has been set at 10%. It is observed how the proposed control recovers more energy in all cases. This difference becomes more evident the greater the adhesion of the surface is. With low adhesion the difference is less because the optimal rate is very close to the 10% established as a constant. In addition, the proposed control does not lead to the blocking of the wheels under any circumstances.

Table 4. Comparison of controls.

Surface/Control Vo (km/h) Vf (km/h) Energy (Wh) High adhesion/Regen. cte. (10%) 80 55.52 4.53 High adhesion/Controlled regeneration 80 22.4 15.93 Medium adhesion/Regen. cte. (10%) 80 55.54 4.53 Medium adhesion/Controlled regeneration 80 36.5 11.37 Low adhesion/Regen. cte. (10%) 80 55.54 4.53 Low adhesion/Controlled regeneration 80 56.1 4.54

5.4. Regenerative vs. Conventional Brake Comparison Finally, emergency braking processes have been simulated. The following simulations allow evaluating the viability of substituting the conventional rear friction brake with the regenerative brake. The ﬁrst test consists of the following sections: 40 m of high adhesion surface and 40 m of low adhesion surface. The initial speed is 120 km/h. The test is similar to the previous cases except that, in this case, the front brake is also being actuated. The pressure in the front axle is controlled by an anti-lock system with slip and wheel angular speed control. The proposed system detects the change in the adhesion conditions (Figure 15a), adapting the regeneration rate to the adhesion conditions of each road (Figure 15b). The regeneration rate is kept at its maximum value on the high adhesion surface. In this zone, the slip on the rear wheels approaches the optimal slip slowly. Once in the low adhesion surface, the regeneration rate is changed to adapt the slip to the optimal one provided by the estimation algorithm (Figure 16). Energies 2017, 10, 1648 12 of 16

5.3. Controls Comparison Simulations have been carried out to verify that the new control manages to optimize regenerated energy. For this purpose, tests in which the vehicle starts at a speed of 80 km/h have been carried out. The test lasts five seconds. At the end of that time, the velocity achieved and the energy recovered are determined (Table 4). The constant regeneration rate has been set at 10%. It is observed how the proposed control recovers more energy in all cases. This difference becomes more evident the greater the adhesion of the surface is. With low adhesion the difference is less because the optimal rate is very close to the 10% established as a constant. In addition, the proposed control does not lead to the blocking of the wheels under any circumstances.

Table 4. Comparison of controls.

5.4. Regenerative vs. Conventional Brake Comparison Finally, emergency braking processes have been simulated. The following simulations allow evaluating the viability of substituting the conventional rear friction brake with the regenerative brake. The first test consists of the following sections: 40 m of high adhesion surface and 40 m of low adhesion surface. The initial speed is 120 km/h. The test is similar to the previous cases except that, in this case, the front brake is also being actuated. The pressure in the front axle is controlled by an anti-lock system with slip and wheel angular speed control. The proposed system detects the change in the adhesion conditions (Figure 15a), adapting the regeneration rate to the adhesion conditions of each road (Figure 15b). The regeneration rate is kept at its maximum value on the high adhesion surface. In this zone, the slip on the rear wheels approaches the optimal slip slowly. Once in the low Energies 2017adhesion, 10, 1648 surface, the regeneration rate is changed to adapt the slip to the optimal one provided by 13 of 16 the estimation algorithm (Figure 16).

(a) (b)

Figure 15. Estimation of road type and optimum slip (a) and regeneration rate (b). Figure 15. Estimation of road type and optimum slip (a) and regeneration rate (b). Energies 2017, 10, 1648 13 of 16

Energies 2017, 10, 1648 13 of 16

Figure 16. Optimum slip, real slip and speed. Figure 16. Optimum slip, real slip and speed. Two additional simulationsFigure have 16. been Optimum conducted. slip, real The slip initial and speed.and final speeds are 80 and 20 km/h Tworespectively additional in simulationsboth cases. In havethe first been case, conducted. an emergency The braking initial andis conducted final speeds on a high are 80adhesion and 20 km/h Two additional simulations have been conducted. The initial and final speeds are 80 and 20 km/h respectivelysurface. in The both rear cases. brake Intorque the is first generated case, an with emergency a conventional braking antilock is conductedsystem (ABS) on and a with high the adhesion regenerativerespectively inbrake both system cases. In(REG). the first Results case, arean emergencyshown in Figure braking 17a. is conductedIt can be seenon a thathigh theadhesion brake surface. The rear brake torque is generated with a conventional ABS and with the regenerative brake distancesurface. Theis lower rear whenbrake thetorque vehicle is generated is equipped with with a conventional the conventional antilock ABS system system. (ABS) This andis due with to the system (REG).factregenerative thatResults the regenerationbrake are system shown rate (REG). in is Figure limited Results 17 to a.are 50 It %.shown can With be in this seen Figure maximum that 17a. the It brakerate, can thebe distance seenregenerative that is the lower brake when the vehicle istorquedistance equipped cannot is lower withbrake when theas efficiently the conventional vehicle as is the equipped conventional ABS system. with thesystem This conventional does. is due Similarly, to ABS the system. fact a second that This thetest is regenerationisdue has to been the rate is limitedsimulatedfact to that 50%. the on With regenerationa low this adhesion maximum rate surface is limited rate, (see the toFigure 50 regenerative%. 17b).With In this this brakemaximum condition, torque rate, the cannot regenerativethe regenerative brake brake as efficientlybrake can as torque cannot brake as efficiently as the conventional system does. Similarly, a second test is has been the conventionalproduce enough system brake does. torque Similarly, to cause a second the rear test wheel is has to been reach simulated the optimal on slip. a low Under adhesion these surface simulated on a low adhesion surface (see Figure 17b). In this condition, the regenerative brake can (see Figurecircumstances, 17b). In this the condition,regenerative the brake regenerative decelerates brakethe vehicle canproduce faster than enough the conventional brake torque ABS to cause systemsproduce thanksenough to brakea better torque control to of cause the rear the wheel rear slip.wheel to reach the optimal slip. Under these the rearcircumstances, wheel to reach the the regenerative optimal slip. brake Under decelerate theses circumstances,the vehicle faster the than regenerative the conventional brake ABS decelerates the vehiclesystems faster thanks than to thea better conventional control of the ABS rear systems wheel slip. thanks to a better control of the rear wheel slip.

(a) (b)

Figure 17. Slip( anda) vehicle speed. High adhesion surface (a); Low adhesion(b) surface (b).

Figure 17. Slip and vehicle speed. High adhesion surface (a); Low adhesion surface (b). FigureFinally, 17. TableSlip 5 and summarizes vehicle speed. the results High obtained adhesion in surface the previous (a); Low tests. adhesion The last surface column (b). also includes the results of tests in which the maximum regeneration rate has been increased to 80%. The regenerativeFinally, Tablecontrol 5 performssummarizes better the on results the low obtained adhesion in thesurface previous due totests. a better The lastand columnmore stable also controlincludes of the the results rear wheel of tests slip. in On which the contrary,the maximum the regenerative regeneration control rate has in whichbeen increased the regeneration to 80%. rateThe isregenerative limited to 50%control cannot performs substitute better the on convention the low adalhesion brake surfaceon high due adhesion to a better roads and since more the stablebrake distancecontrol of and the decelerationrear wheel slip. are On worse. the contrary, However, the if regenerative the maximum control regeneration in which ratethe regenerationis increased, ratethe regenerativeis limited to 50%brake cannot can generate substitute a higher the convention rear brakeal torque, brake onwhich high makes adhesion it perform roads sincebetter the than brake the conventionaldistance and brake.deceleration are worse. However, if the maximum regeneration rate is increased, the regenerativeThese simulations brake can showgenerate that a the higher proposed rear br systemake torque, can be which installed makes in ait real perform vehicle better to cooperate than the withconventional or in substitution brake. of the conventional friction brake. The system could control the slip no matter whichThese surface simulations the vehicle show is moving that the on proposed if the maxi systemmum can regeneration be installed rate in werea real increased. vehicle to However,cooperate with or in substitution of the conventional friction brake. The system could control the slip no matter which surface the vehicle is moving on if the maximum regeneration rate were increased. However,

Energies 2017, 10, 1648 14 of 16

Finally, Table5 summarizes the results obtained in the previous tests. The last column also includes the results of tests in which the maximum regeneration rate has been increased to 80%. The regenerative control performs better on the low adhesion surface due to a better and more stable control of the rear wheel slip. On the contrary, the regenerative control in which the regeneration rate is limited to 50% cannot substitute the conventional brake on high adhesion roads since the brake distance and deceleration are worse. However, if the maximum regeneration rate is increased, the regenerative brake can generate a higher rear brake torque, which makes it perform better than the conventional brake. These simulations show that the proposed system can be installed in a real vehicle to cooperate with or in substitution of the conventional friction brake. The system could control the slip no matter which surface the vehicle is moving on if the maximum regeneration rate were increased. However, batteries with a higher admissible charge current or ultracapacitors are necessary if the maximum regeneration rate is increased [14,16].

Table 5. Comparison ABS vs. Regenerative braking.

Regenerative Regenerative Surface Control ABS Reg. Max. = 50% Reg. Max. = 80% Time (s) 1.39 1.42 1.40 High adhesion Distance (m) 19.54 20.24 20.85 Mean deceleration (m/s2) 11.95 11.71 11.86 Time (s) 3.90 3.76 3.80 Low adhesion Distance (m) 53.51 52.19 52.62 Mean deceleration (m/s2) 4.27 4.44 4.38

6. Conclusions Algorithms for the estimation of parameters and the control necessary to optimize the regeneration in two-wheeled vehicles have been developed in this paper. The parameter estimation is necessary to be able to determine the speed of the vehicle and the road adhesion conditions. The control algorithm allows one, once the previous data is known, to optimize the rate of regeneration in the rear wheel of a motorcycle. Generally, regeneration in two-wheeled vehicles is usually limited to a minimum value, around 5–10% of motor power. This pursues two objectives: on one hand, to limit the charge current in the battery to protect it. Secondly, the possibility of locking the rear wheel in any adhesion condition is reduced. If this occurs, ABS control is required. With the proposal developed in this work, the rate of regeneration on any surface is optimized, blocking the wheel is prevented by always working in the vicinity of the optimal slip. Protection of the battery is achieved by limiting the intensity in those cases in which the regeneration could exceed a value of risk for its integrity. Simulations show that the system optimizes the regeneration rate on every surface and that the energy recovered surpasses the one obtained with constant regeneration rates. Finally, despite the fact that this paper is mainly focused on recovering the maximum energy with the regenerative brake, we have shown that the regenerative brake could substitute the rear friction brake provided higher regeneration rates can be achieved. High regeneration rates require batteries with a higher admissible charge current or ultracapacitors on vehicles. However, there is still a great deal of research to be carried out. Experimental tests have to be carried out to verify the aptitude of this proposal, especially in high adherence conditions. The work reported here is exploratory. Future works will include tests on different surfaces, with different maneuvers and with higher regeneration rates.

Acknowledgments: This work is partly supported by the Ministry of Economy, Industry and Competitiveness under grant TRA2015-67920-R and partly by the University of Málaga. Author Contributions: Juan Jesús Castillo Aguilar performed the simulations and conceived and designed the experiments. Javier Pérez Fernández and Juan María Velasco García performed most experiments and measurements and contributed to the programming of the estimations algorithm. Juan Antonio Cabrera Carrillo Energies 2017, 10, 1648 14 of 16 batteries with a higher admissible charge current or ultracapacitors are necessary if the maximum regeneration rate is increased [14,16].

Table 5. Comparison ABS vs. Regenerative braking.

6. Conclusions Algorithms for the estimation of parameters and the control necessary to optimize the regeneration in two-wheeled vehicles have been developed in this paper. The parameter estimation is necessary to be able to determine the speed of the vehicle and the road adhesion conditions. The control algorithm allows one, once the previous data is known, to optimize the rate of regeneration in the rear wheel of a motorcycle. Generally, regeneration in two-wheeled vehicles is usually limited to a minimum value, around 5–10% of motor power. This pursues two objectives: on one hand, to limit the charge current in the battery to protect it. Secondly, the possibility of locking the rear wheel in any adhesion condition is reduced. If this occurs, ABS control is required. With the proposal developed in this work, the rate of regeneration on any surface is optimized, blocking the wheel is prevented by always working in the vicinity of the optimal slip. Protection of the battery is achieved by limiting the intensity in those cases in which the regeneration could exceed a value of risk for its integrity. Simulations show that the system optimizes the regeneration rate on every surface and that the energy recovered surpasses the one obtained with constant regeneration rates. Finally, despite the fact that this paper is mainly focused on recovering the maximum energy with the regenerative brake, we have shown that the regenerative brake could substitute the rear frictionEnergies brake2017, 10 provided, 1648 higher regeneration rates can be achieved. High regeneration rates require15 of 16 batteries with a higher admissible charge current or ultracapacitors on vehicles. However, there is still a great deal of research to be carried out. Experimental tests have to be carried out to verify the aptitudewrote the of introduction,this proposal, helped especi to analyseally in thehigh data adherence and provided conditio somens. useful The suggestions work reported in the writing here is and exploratory.revision of theFuture text. works will include tests on different surfaces, with different maneuvers and with higherConﬂicts regeneration of Interest: rates.The authors declare no conﬂict of interest.

AppendixAppendix A. A.Experimental Experimental Vehicle Vehicle TheThe motorcycle motorcycle used used in inthe the tests tests is shown is shown below. below. The The table table includes includes the the main main characteristics characteristics of of thethe vehicle. vehicle.

ComponentComponent Parameter Parameter Description Description Motorcycle weight 135 Kg Chassis Type Steel Tubular Height of gravity centre 621 mm Distance between axis 1370 mm Vehicle Wheel radius 300 mm Distance from the COG to the front axle 670 mm Front tire 95/70 R 17 Rear tire 115/70 R 17 Brand Heinzmann PMS 150 Type Axial Flux Permanent Magnet Maximum speed 6000 rpm Electric motor Maximum torque 80 Nm Torque constant (Km) 0.145 Nm/A Maximum power 34.1 KW (46.36 CV) Battery Type LiPo Cell layout 26S5P Total capacity 4.8 KWh Battery Rated Voltage 96 V Maximum discharge current 1250 A Maximum load current 300 A

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